{"cells": [{"cell_type": "markdown", "id": "976839e8", "metadata": {"papermill": {"duration": 0.013333, "end_time": "2022-04-09T14:37:30.852431", "exception": false, "start_time": "2022-04-09T14:37:30.839098", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 5: Transformers and Multi-Head Attention\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2022-04-09T16:34:55.714521\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 [{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/PytorchLightning/pytorch-lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)"]}, {"cell_type": "markdown", "id": "acbeb1c6", "metadata": {"papermill": {"duration": 0.011303, "end_time": "2022-04-09T14:37:30.875464", "exception": false, "start_time": "2022-04-09T14:37:30.864161", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "cdc43926", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2022-04-09T14:37:30.899343Z", "iopub.status.busy": "2022-04-09T14:37:30.898792Z", "iopub.status.idle": "2022-04-09T14:37:34.408514Z", "shell.execute_reply": "2022-04-09T14:37:34.407706Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.523932, "end_time": "2022-04-09T14:37:34.410576", "exception": false, "start_time": "2022-04-09T14:37:30.886644", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"pytorch-lightning>=1.3\" \"torchvision\" \"seaborn\" \"torchmetrics>=0.3\" \"matplotlib\" \"torch>=1.6, <1.9\" \"ipython[notebook]\""]}, {"cell_type": "markdown", "id": "3788f0d0", "metadata": {"papermill": {"duration": 0.011526, "end_time": "2022-04-09T14:37:34.434868", "exception": false, "start_time": "2022-04-09T14:37:34.423342", "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": "58c0e503", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:34.458897Z", "iopub.status.busy": "2022-04-09T14:37:34.458462Z", "iopub.status.idle": "2022-04-09T14:37:37.002927Z", "shell.execute_reply": "2022-04-09T14:37:37.002262Z"}, "papermill": {"duration": 2.558559, "end_time": "2022-04-09T14:37:37.004560", "exception": false, "start_time": "2022-04-09T14:37:34.446001", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/lib/python3.9/site-packages/apex/pyprof/__init__.py:5: FutureWarning: pyprof will be removed by the end of June, 2022\n", " warnings.warn(\"pyprof will be removed by the end of June, 2022\", FutureWarning)\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/tmp/ipykernel_1570/2689201066.py:34: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", " set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Device: cuda:0\n"]}], "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", "# Plotting\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# PyTorch Lightning\n", "import pytorch_lightning as pl\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 IPython.display import set_matplotlib_formats\n", "from pytorch_lightning.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", "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", "pl.seed_everything(42)\n", "\n", "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.determinstic = 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": "db0e6f77", "metadata": {"papermill": {"duration": 0.011684, "end_time": "2022-04-09T14:37:37.028587", "exception": false, "start_time": "2022-04-09T14:37:37.016903", "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": "204d80ad", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.053272Z", "iopub.status.busy": "2022-04-09T14:37:37.052867Z", "iopub.status.idle": "2022-04-09T14:37:37.349899Z", "shell.execute_reply": "2022-04-09T14:37:37.349259Z"}, "papermill": {"duration": 0.311497, "end_time": "2022-04-09T14:37:37.351709", "exception": false, "start_time": "2022-04-09T14:37:37.040212", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/ReverseTask.ckpt...\n", "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": "44446fb8", "metadata": {"papermill": {"duration": 0.011934, "end_time": "2022-04-09T14:37:37.376064", "exception": false, "start_time": "2022-04-09T14:37:37.364130", "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": "899fbed7", "metadata": {"papermill": {"duration": 0.011614, "end_time": "2022-04-09T14:37:37.399352", "exception": false, "start_time": "2022-04-09T14:37:37.387738", "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": "baf076f5", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011622, "end_time": "2022-04-09T14:37:37.422711", "exception": false, "start_time": "2022-04-09T14:37:37.411089", "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 intialize 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": "9070779d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.447403Z", "iopub.status.busy": "2022-04-09T14:37:37.446989Z", "iopub.status.idle": "2022-04-09T14:37:37.451657Z", "shell.execute_reply": "2022-04-09T14:37:37.451096Z"}, "papermill": {"duration": 0.018657, "end_time": "2022-04-09T14:37:37.453028", "exception": false, "start_time": "2022-04-09T14:37:37.434371", "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": "4a475896", "metadata": {"papermill": {"duration": 0.011788, "end_time": "2022-04-09T14:37:37.476729", "exception": false, "start_time": "2022-04-09T14:37:37.464941", "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": "f6b4422f", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.501387Z", "iopub.status.busy": "2022-04-09T14:37:37.501015Z", "iopub.status.idle": "2022-04-09T14:37:37.510585Z", "shell.execute_reply": "2022-04-09T14:37:37.510000Z"}, "papermill": {"duration": 0.023453, "end_time": "2022-04-09T14:37:37.511961", "exception": false, "start_time": "2022-04-09T14:37:37.488508", "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", "pl.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": "8d4dd42f", "metadata": {"papermill": {"duration": 0.011801, "end_time": "2022-04-09T14:37:37.535654", "exception": false, "start_time": "2022-04-09T14:37:37.523853", "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": "738d26e7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.013673, "end_time": "2022-04-09T14:37:37.561299", "exception": false, "start_time": "2022-04-09T14:37:37.547626", "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": "fa28f1cf", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.586727Z", "iopub.status.busy": "2022-04-09T14:37:37.586295Z", "iopub.status.idle": "2022-04-09T14:37:37.594515Z", "shell.execute_reply": "2022-04-09T14:37:37.593932Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.022557, "end_time": "2022-04-09T14:37:37.595903", "exception": false, "start_time": "2022-04-09T14:37:37.573346", "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": "c83e0f14", "metadata": {"papermill": {"duration": 0.011887, "end_time": "2022-04-09T14:37:37.619972", "exception": false, "start_time": "2022-04-09T14:37:37.608085", "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": "0c2468bc", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.012032, "end_time": "2022-04-09T14:37:37.645572", "exception": false, "start_time": "2022-04-09T14:37:37.633540", "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": "c55bfef0", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.670726Z", "iopub.status.busy": "2022-04-09T14:37:37.670245Z", "iopub.status.idle": "2022-04-09T14:37:37.676639Z", "shell.execute_reply": "2022-04-09T14:37:37.676067Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020505, "end_time": "2022-04-09T14:37:37.678049", "exception": false, "start_time": "2022-04-09T14:37:37.657544", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class EncoderBlock(nn.Module):\n", " def __init__(self, input_dim, num_heads, dim_feedforward, dropout=0.0):\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": "0fea27f4", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011884, "end_time": "2022-04-09T14:37:37.702098", "exception": false, "start_time": "2022-04-09T14:37:37.690214", "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": "59e4943c", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.727879Z", "iopub.status.busy": "2022-04-09T14:37:37.726612Z", "iopub.status.idle": "2022-04-09T14:37:37.733398Z", "shell.execute_reply": "2022-04-09T14:37:37.732829Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.020849, "end_time": "2022-04-09T14:37:37.734822", "exception": false, "start_time": "2022-04-09T14:37:37.713973", "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": "a5d9e15f", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011928, "end_time": "2022-04-09T14:37:37.758957", "exception": false, "start_time": "2022-04-09T14:37:37.747029", "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": "97458a86", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.784031Z", "iopub.status.busy": "2022-04-09T14:37:37.783642Z", "iopub.status.idle": "2022-04-09T14:37:37.789832Z", "shell.execute_reply": "2022-04-09T14:37:37.789242Z"}, "papermill": {"duration": 0.02026, "end_time": "2022-04-09T14:37:37.791228", "exception": false, "start_time": "2022-04-09T14:37:37.770968", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class PositionalEncoding(nn.Module):\n", " def __init__(self, d_model, max_len=5000):\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": "61da655f", "metadata": {"papermill": {"duration": 0.012048, "end_time": "2022-04-09T14:37:37.815452", "exception": false, "start_time": "2022-04-09T14:37:37.803404", "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": "35e0dece", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:37.840839Z", "iopub.status.busy": "2022-04-09T14:37:37.840293Z", "iopub.status.idle": "2022-04-09T14:37:38.380487Z", "shell.execute_reply": "2022-04-09T14:37:38.379879Z"}, "papermill": {"duration": 0.554884, "end_time": "2022-04-09T14:37:38.382455", "exception": false, "start_time": "2022-04-09T14:37:37.827571", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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DmUPn/9fKDoU5BAmvkpOWmFgLDzGEHyw9+JEhczf9G36i2btZepLJ2f+Y5yJTUfhSqC5iQl2IG8+hEfA9oWsSWbG98Tkso5lzvgcfhbgEM6EBY31JMrmo5pUhE04MdRwOWqTCuGtiw+Ja0TyN3G77RmZlJoQNj2RC3BiAiCDrArIYLJQ2NhMyWc4D7Q3JDVpg16kbUYuCK5TWCXSiVsSqzOCz5tZ2N0Mt8uCoffH6aFaXYIXRS/VYeF+FPpipmXbukkJ64U07IsweCqQyOy0rtXvE6m6B+j/LUvD9yff4Ha8PzfxcnAplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggOTQgPj4Kc3RyZWFtCnicRY3BEcAgCAT/VEEJCgraTyaTh/b/jRAyfGDnDu6EBQu2eUYfBZUmXhVYB0pj3FCPQL3hci3J3AUPcCd/2tBUnJbTd2mRSVUp3KQSef8OZyaQqHnRY533C2P7IzwKZW5kc3RyZWFtCmVuZG9iagoyOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDM0MSA+PgpzdHJlYW0KeJxFUktuRDEI279TcIFI4ZeQ87Squpjef1ubTNXN4AlgbHjLU6ZkyrC5JSMk15RPfSJDrKb8NHIkIqb4SQkFdpWPx2tLrI3skagUn9rx47H0RqbZFVr17tGlzaJRzcrIOcgQoZ4VurJ71A7Z8HpcSLrvlM0hHMv/UIEsZd1yCiVBW9B37BHfDx2ugiuCYbBrLoPtZTLU//qHFlzvffdixy6AFqznvsEOAKinE7QFyBna7jYpaABVuotJwqPyem52omyjVen5HAAzDjBywIglWx2+0d4Aln1d6EWNiv0rQFFZQPzI1XbB3jHJSHAW5gaOvXA8xZlwSzjGAkCKveIYevAl2OYvV66ImvAJdbpkL7zCntrm50KTCHetAA5eZMOtq6Oolu3pPIL2Z0VyRozUizg6IZJa0jmC4tKgHlrjXDex4m0jsblX3+4f4ZwvXPbrF0vshMQKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE2NCA+PgpzdHJlYW0KeJxFkMdxBTEMQ++qAiUwgAr1rMfzD+v+r4b000F6GEIMYk/CsFxXcWF0w4+3LTMNf0cZ7sb6MmO81VggJ+gDDJGJq9Gk+nbFGar05NVirqOiXC86IhLMkuOrQCN8OrLHk7a2M/10Xh/sIe8T/yoq525hAS6q7kD5Uh/x1I/ZUeqaoY8qK2seatpXhF0RSts+LqcyTt29A1rhvZWrPdrvPx52OvIKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDcyID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXEC+qYm5Qi4XSAzEygGzDIC0JZyCiGeAmCBtEMUgFkSxmYkZRB2cAZHL4EoDACXbFskKZW5kc3RyZWFtCmVuZG9iagozMSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDQ3ID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXJYQVi4XTCwHzALRlnAKIp7BlQYAuWcNJwplbmRzdHJlYW0KZW5kb2JqCjMyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjU4ID4+CnN0cmVhbQp4nEWRS3IEIAhE956CI4D85DyTSmUxuf82Dc5kNnaXqP2ESiOmEiznFHkwfcnyzWS26Xc5VjsbBRRFKJjJVeixAqs7U8SZa4lq62Nl5LjTOwbFG85dOalkcaOMdVR1KnBMz5X1Ud35dlmUfUcOZQrYrHMcbODKbcMYJ0abre4O94kgTydTR8XtINnwByeNfZWrK3CdbPbRSzAOBP1CE5jki0DrDIHGzVP05BLs4+N254Fgb3kRSNkQyJEhGB2Cdp1c/+LW+b3/cYY7z7UZrhzv4neY1nbHX2KSFXMBi9wpqOdrLlrXGTrekzPH5Kb7hs65YJe7g0zv+T/Wz/r+Ax4pZvoKZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvQkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzOQovU3VidHlwZSAvRm9ybSAvVHlwZSAvWE9iamVjdCA+PgpzdHJlYW0KeJzjMjQwUzA2NVXI5TI3NgKzcsAsI3MjIAski2BBZDO40gAV8wp8CmVuZHN0cmVhbQplbmRvYmoKMzQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNjMgPj4Kc3RyZWFtCnicRZA7EgMhDEN7TqEj+CMDPs9mMik2929j2GxSwNNYIIO7E4LU2oKJ6IKHtiXdBe+tBGdj/Ok2bjUS5AR1gFak42iUUn25xWmVdPFoNnMrC60THWYOepSjGaAQOhXe7aLkcqbuzvlDcPVf9b9i3TmbiYHJyh0IzepT3Pk2O6K6usn+pMfcrNd+K+xVYWlZS8sJt527ZkAJ3FM52qs9Px8KOvYKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMyMiA+PgpzdHJlYW0KeJw1UbttxTAM7DUFFzAgfiXN4yBIkbd/mzvaqUjTvB9VXjKlXC51ySpZYfKlQ3WKpnyeZqb8DvWQ45ge2SG6U9aWexgWlol5Sh2xmiz3cAs2vgCaEnML8fcI8CuAUcBEoG7x9w+6WRJAGhT8FOiaq5ZYYgINi4Wt2RXiVt0pWLir+HYkuQcJcjFZ6FMORYopt8B8GSzZkVqc63JZCv9ufQIaYYU47LOLROB5wANMJP5kgGzPPlvs6upFNnaGOOnQgIuAm80kAUFTOKs+uGH7arvm55koJzg51q+iMb4NTuZLUt5XucfPoEHe+DM8Z3eOUA6aUAj03QIgh93ARoQ+tc/ALgO2Sbt3Y0r5nGQpvgQ2CvaoUx3K8GLszFZv2PzH6MpmUWyQlfXR6Q7K3KATYh5vZKFbsrb7Nw+zff8BXxl7ZAplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjE4ID4+CnN0cmVhbQp4nD1QuY0EMQzLXYUaWMB67alnFotLpv/0SPn2ItEWRVIqNZmSKS91lCVZU946fJbEDnmG5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6xWyDQpGDXtiByxw8YDMGZE4siDEpNBv+uco+fXosbPsPxQxSRkg7mNf9Y/fJzDa9TjyeRbm++4l6cqQ4DERySmrwjXVixLhIRaTVBTc/AWi2Au7de/hu0I7oMQPaJxHGaUo6hv2twpc8v5SdT2AplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggODMgPj4Kc3RyZWFtCnicRYy7DcAwCER7pmAEfib2PlGUwt6/DRAlbrgn3T1cHQmZKW4zw0MGngwshl1xgfSWMAtcR1COneyjYdW+6gSN9aZS8+8PlJ7srOKG6wECQhpmCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA1MSA+PgpzdHJlYW0KeJwzNrRQMFAwNDAHkkaGQJaRiUKKIRdIAMTM5YIJ5oBZBkAaojgHriaHK4MrDQDhtA2YCmVuZHN0cmVhbQplbmRvYmoKMzkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDMgPj4Kc3RyZWFtCnicTVG7rQMxDOs9hRY4wPrZvnkueHjFZf82pJwEqURDFEnJw1O6ZMphfUpGSI4uD20aS2y6PDdCU4eKgqlrieqUq5mmzFMsTdDz3lmu5hjge1U31N/0iF4CkVGCVWGBDpA7uGD42WsmbFELIjGGUDOAacIKc7gSMQQZjLVnGJQqDE7VzypX+y+nZdgqsHgwnSI/sppop1+6HHjrKQdC2NyVu3ohTQjujQZjzCxcd6mynQAcTHSZiYxYvA3H0yEMDV6aBqxw1o2YILEbI6UPXgcZ07B3RR51txjxvlvGlLvVz31RfeZd7R8IwRsn+HsByhtdXgplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTYwID4+CnN0cmVhbQp4nEWQORIDMQgEc72CJ0hcgvesy7XB+v+pB9ZHoukCNBy6Fk3KehRoPumxRqG60GvoLEqSRMEWkh1Qp2OIOyhITEhjkki2HoMjmlizXZiZVCqzUuG0acXCv9la1chEjXCN/InpBlT8T+pclPBNg6+SMfoYVLw7g4xJ+F5F3Fox7f5EMLEZ9glvRSYFhImxqdm+z2CGzPcK1zjH8w1MgjfrCmVuZHN0cmVhbQplbmRvYmoKNDEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMzQgPj4Kc3RyZWFtCnicLVJLcsUgDNtzCl2gM/gH5DzpdLp4vf+2kpNFRg5g9DHlholKfFkgt6PWxLeNzECF4a+rzIXPSNvIOojLkIu4ki2Fe0Qs5DHEPMSC76vxHh75rMzJswfGL9l3Dyv21IRlIePFGdphFcdhFeRYsHUhqnt4U6TDqSTY44v/PsVzLQQtfEbQgF/kn6+O4PmSFmn3mG3TrnqwTDuqpLAcbE9zXiZfWme5Oh7PB8n2rtgRUrsCFIW5M85z4SjTVka0FnY2SGpcbG+O/VhK0IVuXEaKI5CfqSI8oKTJzCYK4o+cHnIqA2Hqmq50chtVcaeezDWbi7czSWbrvkixmcJ5XTiz/gxTZrV5J89yotSpCO+xZ0vQ0Dmunr2WWWh0mxO8pITPxk5PTr5XM+shORUJqWJaV8FpFJliCdsSX1NRU5p6Gf778u7xO37+ASxzfHMKZW5kc3RyZWFtCmVuZG9iago0MiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDcwID4+CnN0cmVhbQp4nDMzNlMwULAwAhKmpoYK5kaWCimGXEA+iJXLBRPLAbPMLMyBLCMLkJYcLkMLYzBtYmykYGZiBmRZIDEgujK40gCYmhMDCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMjAgPj4Kc3RyZWFtCnicNVJLbgUxCNvPKbhApfBPzvOqqou++29rE70VTDBg4ykvWdJLvtQl26XD5Fsf9yWxQt6P7ZrMUsX3FrMUzy2vR88Rty0KBFETPViZLxUi1M/06DqocEqfgVcItxQbvINJAINq+AcepTMgUOdAxrtiMlIDgiTYc2lxCIlyJol/pLye3yetpKH0PVmZy9+TS6XQHU1O6AHFysVJoF1J+aCZmEpEkpfrfbFC9IbAkjw+RzHJgOw2iW2iBSbnHqUlzMQUOrDHArxmmtVV6GDCHocpjFcLs6gebPJbE5WkHa3jGdkw3sswU2Kh4bAF1OZiZYLu5eM1r8KI7VGTXcNw7pbNdwjRaP4bFsrgYxWSgEensRINaTjAiMCeXjjFXvMTOQ7AiGOdmiwMY2gmp3qOicDQnrOlYcbHHlr18w9U6XyHCmVuZHN0cmVhbQplbmRvYmoKNDQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxOCA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKNDUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxMzMgPj4Kc3RyZWFtCnicRY9LDgQhCET3nKKOwMcf53Ey6YVz/+2AnW4TYz2FVIG5gqE9LmsDnRUfIRm28beplo5FWT5UelJWD8ngh6zGyyHcoCzwgkkqhiFQi5gakS1lbreA2zYNsrKVU6WOsIujMI/2tGwVHl+iWyJ1kj+DxCov3OO6Hcil1rveoou+f6QBMQkKZW5kc3RyZWFtCmVuZG9iago0NiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDM0MCA+PgpzdHJlYW0KeJw1UjluBDEM6/0KfSCAbtvv2SBIkfy/DanZFANxdFKUO1pUdsuHhVS17HT5tJXaEjfkd2WFxAnJqxLtUoZIqLxWIdXvmTKvtzVnBMhSpcLkpORxyYI/w6WnC8f5trGv5cgdjx5YFSOhRMAyxcToGpbO7rBmW36WacCPeIScK9Ytx1gFUhvdOO2K96F5LbIGiL2ZlooKHVaJFn5B8aBHjX32GFRYINHtHElwjIlQkYB2gdpIDDl7LHZRH/QzKDET6NobRdxBgSWSmDnFunT03/jQsaD+2Iw3vzoq6VtaWWPSPhvtlMYsMul6WPR089bHgws076L859UMEjRljZLGB63aOYaimVFWeLdDkw3NMcch8w6ewxkJSvo8FL+PJRMdlMjfDg2hf18eo4ycNt4C5qI/bRUHDuKzw165gRVKF2uS9wGpTOiB6f+v8bW+19cfHe2AxgplbmRzdHJlYW0KZW5kb2JqCjQ3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjUxID4+CnN0cmVhbQp4nC1RSXIDQQi7zyv0hGan32OXK4fk/9cIygcGDYtAdFrioIyfICxXvOWRq2jD3zMxgt8Fh34r121Y5EBUIEljUDWhdvF69B7YcZgJzJPWsAxmrA/8jCnc6MXhMRlnt9dl1BDsXa89mUHJrFzEJRMXTNVhI2cOP5kyLrRzPTcg50ZYl2GQblYaMxKONIVIIYWqm6TOBEESjK5GjTZyFPulL490hlWNqDHscy1tX89NOGvQ7Fis8uSUHl1xLicXL6wc9PU2AxdRaazyQEjA/W4P9XOyk994S+fOFtPje83J8sJUYMWb125ANtXi37yI4/uMr+fn+fwDX2BbiAplbmRzdHJlYW0KZW5kb2JqCjQ4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTc0ID4+CnN0cmVhbQp4nE2QSQ5DIQxD95zCF6iEM8DnPL+qumjvv61DB3WB/OQgcDw80HEkLnRk6IyOK5sc48CzIGPi0Tj/ybg+xDFB3aItWJd2x9nMEnPCMjECtkbJ2TyiwA/HXAgSZJcfvsAgIl2P+VbzWZP0z7c73Y+6tGZfPaLAiewIxbABV4D9useBS8L5XtPklyolYxOH8oHqIlI2O6EQtVTscqqKs92bK3AV9PzRQ+7tBbUjPN8KZW5kc3RyZWFtCmVuZG9iago0OSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDc1ID4+CnN0cmVhbQp4nDO1NFIwUDA2ABKmZkYKpibmCimGXEA+iJXLZWhkCmblcBlZmilYWAAZJmbmUCGYhhwuY1NzoAFARcamYBqqP4crgysNAJWQEu8KZW5kc3RyZWFtCmVuZG9iago1MCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxNSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNyAwIG9iago8PCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9DaGFyUHJvY3MgMTggMCBSCi9FbmNvZGluZyA8PAovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDYgL3BlcmlvZCA0OCAvemVybyAvb25lIC90d28gL3RocmVlIC9mb3VyIC9maXZlIC9zaXggL3NldmVuCi9laWdodCAvbmluZSA3MiAvSCA4MCAvUCA5NyAvYSA5OSAvYyAvZCAvZSAxMDMgL2cgL2ggL2kgMTA4IC9sIC9tIC9uIC9vIDExMwovcSAvciAvcyAvdCAvdSAvdiBdCi9UeXBlIC9FbmNvZGluZyA+PgovRmlyc3RDaGFyIDAgL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udERlc2NyaXB0b3IgMTYgMCBSCi9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdIC9MYXN0Q2hhciAyNTUgL05hbWUgL0JNUVFEVitEZWphVnVTYW5zCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDE1IDAgUiA+PgplbmRvYmoKMTYgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnROYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovSXRhbGljQW5nbGUgMCAvTWF4V2lkdGggMTM0MiAvU3RlbVYgMCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL1hIZWlnaHQgMCA+PgplbmRvYmoKMTUgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTggMCBvYmoKPDwgL0ggMTkgMCBSIC9QIDIwIDAgUiAvYSAyMSAwIFIgL2MgMjIgMCBSIC9kIDIzIDAgUiAvZSAyNCAwIFIKL2VpZ2h0IDI1IDAgUiAvZml2ZSAyNiAwIFIgL2ZvdXIgMjcgMCBSIC9nIDI4IDAgUiAvaCAyOSAwIFIgL2kgMzAgMCBSCi9sIDMxIDAgUiAvbSAzMiAwIFIgL24gMzQgMCBSIC9uaW5lIDM1IDAgUiAvbyAzNiAwIFIgL29uZSAzNyAwIFIKL3BlcmlvZCAzOCAwIFIgL3EgMzkgMCBSIC9yIDQwIDAgUiAvcyA0MSAwIFIgL3NldmVuIDQyIDAgUiAvc2l4IDQzIDAgUgovc3BhY2UgNDQgMCBSIC90IDQ1IDAgUiAvdGhyZWUgNDYgMCBSIC90d28gNDcgMCBSIC91IDQ4IDAgUiAvdiA0OSAwIFIKL3plcm8gNTAgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNyAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9DQSAwIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4KL0EyIDw8IC9DQSAxIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4gPj4KZW5kb2JqCjUgMCBvYmoKPDwgPj4KZW5kb2JqCjYgMCBvYmoKPDwgPj4KZW5kb2JqCjcgMCBvYmoKPDwgL0YxLURlamFWdVNhbnMtbWludXMgMzMgMCBSIC9JMSAxMyAwIFIgL0kyIDE0IDAgUiA+PgplbmRvYmoKMTMgMCBvYmoKPDwgL0JpdHNQZXJDb21wb25lbnQgOAovQ29sb3JTcGFjZSBbL0luZGV4ZWQgL0RldmljZVJHQiAyNTUgKP7+/v7+/f39/f78+/77+fv7+/769/r6+v749fn5+fj4+P738v718Pf39/X19f707v7z7P7x6vT09PPz8/Ly8vHx8f7w5/7u5f7t4+/v7+7u7u3t7f3r4f3q3/3p3P3n2v3m2P3k1uzs7Orq6unp6ejo6Ofn5+bm5uTk5P3j1P3i0f3gz+Pj4+Li4uHh4eDg4P3fzf3dy/3cyd7e3v3bx/zYxPzWwfvUvvvSvPvQufrOtt3d3dvb29ra2tjY2NfX19XV1dTU1NLS0tHR0c/Pz87OzvrMtPrKsfnHrvnFq/nDqfjBpszMzPi/o/i9ofe7nve5m/e2mPa0lvayk/WwkMvLy8nJycjIyMbGxsXFxcPDw8LCwsDAwL+/v729vby8vLq6urm5ube3t7W1tbOzs7Gxsa+vr/WujvWsi/SqiPSohvSmg/OjgPKgfvGefO+beu6YeO2WduyTdOuQcuqNcOiLbueIbK2traurq6mpqaenp6WlpaOjo6GhoZ+fn52dnZubm5mZmZeXl5WVlZOTk5GRkY+Pj42NjYuLi4mJiYeHh+aFauWDaOSAZeJ9Y+F7YeB4X991Xd5yW91wWdttV9pqVdloU9hlUddiT9ZgTYSEhIKCgoCAgH19fXt7e3l5eXd3d3R0dHJycnBwcG1tbWtra2lpaWdnZ2RkZGJiYmBgYNRdS9NaStFXSdBUR85RRs1PRMxMQ8pJQslGQcdDP8ZAPsU+PMM7O8I4OsA1OL8yN15eXltbW74wNrwtNLsqM1lZWVdXV1RUVFJSUlBQUE5OTkxMTElJSUhISEVFRUREREFBQUBAQD09PTw8PDk5OTg4ODU1NTQ0NDExMTAwMC4uLiwsLCoqKlwoXChcKLknMrgkMbYhL7UfLrQcLbIZK7AXKq0WKqoVXCmnFFwppBNcKKESXCieESebECeZECeWDyaTDiaQXHIljQwligskh1xuJIQJI4EII34HInsGIngFIXUEIXIDIG8CIGwBHyYmJiQkJCIiIiAgIGkAH2cAHx4eHhwcHBoaGildCi9EZWNvZGVQYXJtcyA8PCAvQ29sb3JzIDEgL0NvbHVtbnMgMzI3IC9QcmVkaWN0b3IgMTAgPj4KL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0hlaWdodCAxNjQgL0xlbmd0aCA1MSAwIFIgL1N1YnR5cGUgL0ltYWdlCi9UeXBlIC9YT2JqZWN0IC9XaWR0aCAzMjcgPj4Kc3RyZWFtCnic7ZwLeFVVfsWxNDBASSM4VhJTaMhAQJJGoFQEDWEARVDGVkZGSAciL4tggiJSKhCeJr4q+EYMiTjQ8QFSWqRgxWl4DAxDAUFRgXZ0kI48Ooi8o/Nb+Z/ck5MXES9fbu+31/f5qeFmn73X/7DW+u+zz21w2WWXbQXvv//+D8DevXu//PLL/qCoqOhrkJ2dPX/+/PPgqaeeuhvws8WLF98CTpw4wcf3tmvXjl9+nzEaNmz4W3D48OHW4L333jt9+nRvsHLlynPg3nvvnT179jcgLy/vPsDPVqxY0RecOXNmHejYsePRo0c/BTExMb8Cu3btSkpK+hgwq5tAcXGxZjV8+PCnn35as3ryySdHAQZ9+eWXbwPM6j9ASkrK7t27fwkaNWp0EHzxxReJiYn/CU6dOtULrFq1ign8PZg7d65mNX369NzcXA365ptv/hgwq5+D9PT0Y8eO/QY0adLk12Dnzp3x8fGfgOPHj18PlixZ0sDx6HiMJB4ZYBaYMGGC1vrWW2/16dOH9Z9ev379X4AjR4589tlnDQDLEl/JyckfffTRl8Z2kbEN2dkLFiwwsp8aOXKk1lpYWDhgwACWdeLdd9+F7Hasa8uWLQ2N7cPGtpEttv8FGNlzgJGdl5OTw6DLwcCBA1nWmbVr13YMsQ3ZMdu2bdsVYptlHYfsV0CI7KeN7CdHjx5tZL9sZJ8Ikb3byG4UIjvRyD6VkZHxr8DYnmtsQ/b0iRMnGtli+yyAbMej49HxGIU84jOLQGlp6Q4wYsQIJvLfgE9Krzt06DBt2rQPAAPPBFxmzZo1UmNWcivIysraDpg4pKLETV544YWvABVAtnuCkpIS6TicpaWlTQLQoEI888wzLVu2RKSX8MsQsm3IkCG33367XIG5awmXX3450xfTe/bs+Qdw7bXXUsWPuPr+/fv/GkCPqstiCgoKvgc8Uzt06NDYsWNhfyC109WXLVuWkJCgOkP2/4HHH3+c4lKYtfzpJpCZmUkVfwdOnjwplnxqcJYRlanpEKDG8eh4jCgeGUShqkuXLvcDVoh0PAdiY2M9nSExDQWDBg1CKT9jkNWrV18FCIPeCv8RXHPNNYRJ8XvgwAGFKrIml5d8LFy48I8BWZEJ/i9Abn4I0CWurojWtm1badjvy6D/4gf/DPhjPqTP8iv6TQZgmBWMxqAaG21Ej0dyPS6rqzMJpnINM2Jimh/T/DPApPlT5VsWchdgWSxOa2SpWjELZ/liATLESYAabosjfOJ5wOe9HOtT43h0PDoeo5BHxHQy2LhxI3paynVZli5z9dVX01jP//zzz7k8/eUxoqXo6dGjB9orbecy/wUwAnXmjPX2228rwaHyakYffPBBZF8tOXPR2MQ/QunfAezjCWBVkeZjKerpU1NTcQvY38nY+hf/16lTJzXzfEIc83kVE4+Ii4v7KcC8zhq4inJss2bNuPKDTIB5aDrMSn03U5w3b54mzNia/ksvvQRD/QDWpAUyNg4nJ2LtogcLUiGYOuxgaKPhiuw4kYoxtpzynXfe+VuAgTkeHY8RxWNgxp1sxlqaN2OWy6o1YzjQdBmVGYseWPJnfM5mDJuDbMbz/BnDOzPuQhECMz7oz5ja1XnG6mLRJcZGAPcsXryY9i2jW7duBD5pGGNLSMmmbdq00e7X0qVL1eShZps3b1YGJeLSBU/Yt28fanvCdv/uAWRZmj5t1zG2YiHSKWkms7722mu0jF8wtv5FQE5PT6d/7kP0/RDwecej4zGieGTGg0F+fj6X2cxi+KGuy4LERt++fQkZdGu3M5ZkjzUxnLSKiWg/5eabb24KkFQUR1vr6OA3NhMI16p79er1p4A+bfny5UoedI3nTXXp8RRzrrvuuisA1YIXdYgUTGPs3r173bp1dF/TkGU4v3r8+PHaGKLrgwONAXNQ/M6jjz7auXPnJEAIUqdJFamKsgnJSBFqypQpKSkp2sKiWyUErab/O2eAGm6Bt6kbrR4fSuGztJHLuL2+NjDaq2DUqFEEsrYQy/2jy1rdzjMXx6Pj0fEYhTx6tG3atOkRMHDgwIYNG/4IIKb/A3za9u/fj3g/gxM1btz4J4DObAvwaSspKaEJm52ZmfknYMyYMUi/vAop92j7+OOP/wl0794dUW9JkiwsLAzShptpy7lnz55q5saNG4c1kWE/92nTbt5jjz3WtWtXTCcBv1Nrt337dmjTGNCmTnPq1KmkUaymTU5Ozr+BAG1rAL1ex44dtVH/0EMPYUZLfdp27NjxM4DfJQM8ZdKkSf8ODh8+rAGsu9W+3n333ffnIC0tzfHoeIwoHtFHtKcH83kR0GahBWeMGw0JPXClUfPy8oYA9IEFaiOcxUirIPsosIVqI5x5Ip37IBHFUStJ0FQ2Rf0QXG1AE/9Uu2/KoAAGldKwOXPmZGVltQfIKbp78wMPPLBixQoquJ8lfGWyyoVOQifREopXIlRqG8m4xFZFUfRY2kYKZFytgEsom8I0UosN5I8YMYKFpyH7yDIs57zxxhs6agDTXsXh6pg9+hdZRGhSsdQ5Pj5eiZQ6o8A6Y0CA9S7heHQ8Oh6jkEf+0aYWbTCJcSPxjQ5UXWWLFi30RAy3WLBggXSZ3Ce74RpcSS0vIU6a/9xzz2EH4whV+M+NAHkmSb5EeES5vZLoElyUhCpXIA8qs7Zq1SoxMVGmRoSUF3z66ad09ufsEup8WRoJk+T6Goqux4OYoFr17OzsgoICPGk9VT9todeK/gEBVlWfOXMmViibuPLKKwcAEiZF1+M+K7pXdZ1HoOg6aoDx9OvXrxkgQmp7b+7cubIsihwqepkn8wN+rIMBVF15k19xPDoeI4pH9FHb2fR/el5E/CsuLv4F4MPal/KSnVEhKSNbkTUlQcgpKW0qDaGWFhsbi26iONfR/z0OUB06J7WSCKa3T27acw6GjtjBob17974FyJT3gptuuok0RjkaI6dSosmTJyOpasnIpjptAFFnQ1PSf/ED/XzDhg18SAmYMKjfZACG0WEdBlWduQRSqwObXFZX506wGZ33tuZglMmq3UPatQIWQtLtzrJY3F8CKqMVU0Lipo4OQIY4gRrHo+PR8RiFPHo5Ea3WOUKyIilLpwRnzJihpwkZGRmkJolv586dtX1GpHv++ee18USC0w4+3uPJtpcfSV7qiD/55BPCl1ry3Nxc5U26WWxCG0/YmB7tEfWKioqIrO+R9nQQ0WvVbUpSdIwKa1K0pJl/GAwbNkwnr2jOSYzdwB133IGDPPr666/T/2pDwCtaaEbn7fH+UWwSy1LknD9/vp4r9O/fn+77jwB5VQeq8EesQz6IReocuN1GZ0O3ke4jPXDEj+FRUXTKlCnK1N4+hePR8RgxPKKP5LhW/O3XCxgwhwAoGhUWFkoE0SUSmZ4ookQKg9YGeWvVZViuHgpyXQRDb28wERSkgO4JwdVlyJSS4KZNm9Lu6VgyMU0P/+AX1YKC18mOUi5oIJRq1cZEORXWDUrDqJEOKBI0aeS0iY2UkW8XUF3UTGcJyLEkxitjYmISEhLU8RJUdXaRIqCeOohN9kNON9hrJZI3W9XXoVVpWfo5f6xNPWijl9VvMoD25rkFUGDVMykpSWpOTR2PjseI4tFPNeqvECkEQFsZqJb6Kxo8ZIww8RDSpJezaINQJ53vb926tWIObZ3khpyAbmoDiF4R6Txw8OBBkoRaSTTT6/X83KOjBkgZqqXdHLRN8efZZ59F65SAbrvttr8CxBYk9fsgNTVVD97p1tCkKcQzJFyb2EiZNuUJIN4xPW9b3Bc16iztJRwhtWollyxZoq2tvLw8FFFHmXr16qVt8ebNm2MDehJ2/fXX/w3IycnRQzi60lWrVr0LaCql/IQm6uxtP3lwPDoeHY9RyCM+0wIkJyer84EojGE4gDki5AzaskWLFunJOvKsU75MBu3VOyJMzlPpUnvk7s9fYm0nHvUJPqjP82v8ssZAtjUiVKDcem8LauQF9F1ES3WZ2JKeKDZo0ABr0tuzXbt21fuuFE1vqE6cOPGJJ55QNqX7ZJGrKATGo10xKiMnYq1UUNaEQ/lV9F1LJ6RgQ/ve1FP00BTu27dPW4HYnk5Pc1/oNOSkSZPGjx8vY7zllluUWTt06ICT6cU0qNfZAy8/Oh4djxHDo7/y0tDKd9vKt4VWrm1hFq63m1g59E6xles4ANRr5RTCFt4gLi7OX7layXHjxuktAFaOAOpFKRaubRVWTmL8ra1cqZGVs7qTtnJ/1d6zd22Q8wl9kJUjv78JrHz58uV664BEOim0cgVKFi7Ns5XHAlt4WmZm5p133qned/LkyTp7QGjGDdQX0odqy4s8rNOQGAb5WHvtyK8Sc+CW8WTY8eh4dDxGIY/4jI4vEtH0cI2YdsMNN+h5IR0kiXEYZHEZvbCFHajvJnnReutqpDc91OeCetJI5IR2bSvRi6sQ9h6oWnI7VV5a5fJ++fiEsh8lJAWqW2cMbXOzCgqpbpg4q2KSaIsB9czPz9cL79RUZ75HjRqFNyoM0k7DUCaNNUlXJkB19RYuJGJZepqPTVDjroREfEN1vueee5SAKTUhWC+7UA51/SRanQPAw+j99bUe2Ji+hARPovB60ohJ/d7szPHoeIwoHquEPtakxcOBJCGwJvWLrAnmRCA8ak2kQG2T2Zr0GIhuS2Fw6NChrEmtZM+ePXW+p23btldccYXWRO10QvKqq65CuDqD7t2765QO6XDs2LFI23jW9BggvSGpWhNxTmtas2aNGjR/TcikNuVZE7KpZpNMKCG1XvR8dTC1PcknWL9ogA09+IIbGNKBTVRbJ5RgTzKO9q5cuZKg+gaBUpt0CD0KrG8gIsqqgbz11lsdj47HiOKRNWlptIbaFmet7du3Vyxg8fr2KpQSNpRvsrKyRM/IkSNpGSWYRAV9YQWaqUc+CxcuRDdF7KuvvqqdGNRs/fr1agOhXu9CbNmyhVroAZKJ6E5qZDr6AcIoffS11HvXoIqeBqseKrwqz++fsuLvMZHdtWuX2lFuAQW4rVu3MgcpOTPSszXuhGXLlmlPC61fDAg93A96SWP27NnqiR9++GHlO+6L7OxsHfhBfolKd/bt2xcFxkduoJPW+R6MxfHoeHQ8RiGP1c2u1Lcbm92HluwUC2122212en0L79FRl5KSEvxHzwvpqsQjfWRRUZGOFBA5dfIGJyKD6oEbgU/vzt999910ZUqpQ4YM0ZN4POnGG29Ul4kt6RFjQkIC1tQcxMTEaKOqWbNmlwNSYJs2bfQ9FARBbcrTgQ4YMEBHKekG9fIqXpWbm/sAwK60q40/YFkvAAxDm+80p6tXr9bJTvpKfSkQ5nXgwAGdJcBq9D7uoUOHfmdBERfSBpvZ2HHsyZzsK29PDjgeHY8RxSP6qO+tozXUtjjtYXx8vM7gtGvXTn/1UUpaLB1XNrH8IculZZRgEhAlGvRjequdzgzdVI+GrEw37URn1ErCoR4soT+QKiVCRLUfjjShUGr3YF+SRSEohzSMwiisUiZPUqmb13PqGJIpq3TQxPVDam366vWhZ32N9VCL1FYRXf9W8rX3tH9HmQTrpjIV1o3leHQ8Oh6jkMfah/va5uMNFzQeL6nZcF5a01q9wAYBJMZfmg2JG3MiPYmHNrFn8U2MQqz4hWYLcQVQrwpQCKxJVaE4qhGl0tczUzfKpypSTNWU0pLqVGfKrWRH8bkFdCdgV7ovuD2wLN0r3DK6c7iBGjVqpJOdcXFxerk2MTGxdevWetklPT1dXX+3bt10rrlfv379+/fXCc5BgwapnR4zZgxOpuMKEyZMwM1y77//fsej4zGieGQ4ne1t3LixtsVjY2Nbtmz5fZNJvSfODJiIXp0k0qlfJNwxR001IyNDO8/MXScnTTd13JHuSS9M0keSDtVK0lXpXQj6K0KjOq2pU6eqsSRJ0n3pHMCsWbNmm5hCoLpM5JRGc+GiRYvgln6zkChabLKqtxdMWXXe2cR1HcUxff2F9aEbTGPVjlJInQAyqVWBTW0ltxR+pyEouqa7ZcJr2mvyW1F/DX4L63h0PDoeo5DHOieqkOmU+uP43nParuZd+gPPgwIpy2a8w9xIK2FB5ke/YpFbzJRY+CbzJbEBKVCjaGnuJHsSe+ZQYtRMqhiaIVvNPNSrAhSCcjxifqUaUSoKpu0wyqcqUkxKqj6fAqvOlJui62AAt4DuBG4I3RfcHtwkfc3HdOdwA5mT9eCm0r3FLeZ4dDxGFI+ePhKoGptGNm/eXKdgCFr6qiyUslWrVlLKpKQk5bDk5GRaRgkm2ayTiaZOTvq6SQ+prxIkyPXu3VuRrk+fPprF4MGDmZI2lJmc11gOGzaMSWcRBH9qYspi1GWyLOVEX1LJjlNNVvVaF1SQKWeYus4ydZ0zZ44i57x58x4xkSWGvmg6q9fqfaklqBaZ3L7yyitLTHG1Lb60DMtMeJVv165d62uv17dqf58gvMFgKiwZdjw6HiOKxwvoY81iWUEwq+hmBe0M7JacqqKi5TpaJqRei+npaWVJ9XpOX1m97nOrwWtCNxt8jQUsvMSXWlNbyS1E/dwUV+wZjz8z4RW/0OxpL9QXmv7q6CJVMQV+kVLlmww7Hh2Pjsco5BGf0WFksxp5TZMmTZqa3TQ3y8Fx9A6GmY5cJ95g3pNk3pNM99jOYBYkD0pNTU3zbCjkRHpN1MyozI3K/KgXoay3mVIfgzWaA31rMneSPd1lfecwg5mUXGq4wbwq27xqpPnVaPMroBfTzLXKbMv6UzOuaQHzEmbOnOl5mNlYmY+FrCzfUGB25nh0PEYUj99GH+smmtVqZ3UKGhDRoJbWJKkBZa2grr7A+hpbWWbLpdZX24Dg/joguh587fX6Vl+CNxk2GhyPjkfHYxTy6PuM12Z7nXYTQ9Om5aYTa2hRBs94vA68iv94/bjfkntO5JlRe0NKiudHMqRUz5Qq2pJ8ybMmc6cyewo4lJlUmUtZM1/Fq8yuBptl/ThgWz8x3GUYWgbfv8oMzOv8K9rYCH8bwHczx6PjMaJ4/M76eBHSWSctrUlSaxHXqgJbWWarVduA4FbR3aD2BvXXl2HHo+Mxonj09LGyRJbLpK+UIbEsbxkra2a5bvrSGVBPD0ERDelomZAaAnL6g0C/2b6KsnqoqK8VRLaLj4DUlsttSHCriG658Bp6B/Q3IMEeHI+OR8djFPJ4KX3m26DOnlRnh/o2lnUB86rdxgTHo+Mxonj09bGCRtaglFUEs4JoBnSzgnbWoKA1CWkVPfUlNSirvrLWJLBVdNaX2kpyWy645aisu6mpVfQ31L56Mux4dDw6HqOQx/r2l7DhIo0qTB7meHQ8BlHPPAb1sRqVDCpltYJZrW4GpLNaBa1GR4NaWq2kVlHWagW2ssZWldpqBLd64Q1ob7US7H3Ph+PR8eh4jDIe69se/l/gwp7keKwLHI/hQR14rE4fa5LJWgTzQtpZg4LWoqW1SGpNylqbyF5AbmsQ3VrkN6jCjkfHY0TxWN/SEyVwPIYHjsfwwPEYHtTmM3V2nbr5T13NqG6+VDeXqqtl1d3AanAyx6PjMaJ4rG9hiRI4HsMDx2N44HgMD+ruMxdnPN/NjS7Snr6TZV2chzkeHY8RxWN9C0uUwPEYHjgew4PvoI+XRDoviapeEsENwvHoeHQ8RiGP9S3QUQLHY3jgeAwPLo0+1q+Q1ofcOh4dj47HKOSxvgU6SuB4DA8cj+FBPevjxaMelbU6OB7DA8djeOB4DA+cz4QHjsfwwPEYHvwB7ZHo5wplbmRzdHJlYW0KZW5kb2JqCjUxIDAgb2JqCjU4MjkKZW5kb2JqCjE0IDAgb2JqCjw8IC9CaXRzUGVyQ29tcG9uZW50IDgKL0NvbG9yU3BhY2UgWy9JbmRleGVkIC9EZXZpY2VSR0IgMTYyICj//v7+/v78/Pz/+/r7+/v/+fX4+Pj/9/P+9O/+8+z39/f19fX+8Oj+7+by8vLx8fH+7OH+6d3v7+/t7e3r6+vq6ur+6Nv+5db949T94dDn5+fl5eX938793Mn92cT818L807zk5OTh4eHg4ODd3d3c3NzZ2dnW1tbU1NTR0dH70Ln7zLT6yrH5xqz5wqfQ0NDNzc34v6T4u573uZz3tZb2s5TLy8vIyMjFxcXExMTBwcG/v7+8vLy7u7u3t7ezs7P2r471qon1qIbzpIHyoX/wnHvvmXnsk3SxsbGtra2rq6unp6elpaWhoaGdnZ2bm5uXl5eVlZXrkXLpi27mhmrlg2jjfmTie2Lfdl7ec1xckZGRj4+Pi4uL3G5X2mhT2GVR1mBN1V1M0lhJ0FVIzk9Fy0lCyUdBh4eHhYWFgICAfn5+eXl5d3d3c3NzcHBwbGxsZ2dnZWVlYGBgXl5eWlpaV1dXU1NTTk5OTExMSEhIRkZGxkE+xT49wjg6wTY5vjA2uyo0ulwoMrciMLYfLrMZLLEYK6sWKqUUXCmiE1wonBEnmRAnkw4mkFxyJooLJYQJJIEII0JCQkBAQDw8PDg4ODY2NjIyMjAwMCwsLCoqKiYmJnwHInkGInMEIXADIGoBHyIiIiAgIBwcHBoaGildCi9EZWNvZGVQYXJtcyA8PCAvQ29sb3JzIDEgL0NvbHVtbnMgOCAvUHJlZGljdG9yIDEwID4+IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9IZWlnaHQgMTYzIC9MZW5ndGggNTIgMCBSIC9TdWJ0eXBlIC9JbWFnZSAvVHlwZSAvWE9iamVjdCAvV2lkdGggOCA+PgpzdHJlYW0KeJw9wedaCAAAQNFLoRJSKbQLTQ2hoa0SRYOirIxUKEpICUVoKYpoKD1pf+7nHLbEP7EpNsQf8Vssi1/ip1gSi+KH+C4WxIh4I4bFa/FKvBRD4oUYFAPiuXgm+kWfeCqeiMeiV/SIbnFL3BQdolk0iUZxRVwWDaJeXBIXRZ0oF2WiVJSIc6JYFIlCUSDOilyRI9JFmkgVJ8RxcUykiGSRJGLEUXFIRIoIES4OiP1irwgRu0Wg2Cl2iP8CxC6xRwSJYBEq9okwcVBEiWhxWBwRsSJOxIsEkSgyRKbIEidFtsgTp0S+OC3OiApRKarEeVEtakStuCBaxFVxTbSKNnFd3BDt4ra4I+6KTnFP3BcPxEPRJR6JUfFWjIl34r34IMbFhPgoPolJ8Vl8EV/FlJgWM2JWzIlvYl6siFWxJtbFX20DycmcqAplbmRzdHJlYW0KZW5kb2JqCjUyIDAgb2JqCjMyNQplbmRvYmoKMiAwIG9iago8PCAvQ291bnQgMSAvS2lkcyBbIDExIDAgUiBdIC9UeXBlIC9QYWdlcyA+PgplbmRvYmoKNTMgMCBvYmoKPDwgL0NyZWF0aW9uRGF0ZSAoRDoyMDIyMDQwOTE2MzczOCswMicwMCcpCi9DcmVhdG9yIChNYXRwbG90bGliIHYzLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1Y2VyIChNYXRwbG90bGliIHBkZiBiYWNrZW5kIHYzLjUuMSkgPj4KZW5kb2JqCnhyZWYKMCA1NAowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAyMDI4OSAwMDAwMCBuIAowMDAwMDEyMDg5IDAwMDAwIG4gCjAwMDAwMTIxMjEgMDAwMDAgbiAKMDAwMDAxMjIyMCAwMDAwMCBuIAowMDAwMDEyMjQxIDAwMDAwIG4gCjAwMDAwMTIyNjIgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwMzQyIDAwMDAwIG4gCjAwMDAwMDE0ODIgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAxNDYxIDAwMDAwIG4gCjAwMDAwMTIzMzMgMDAwMDAgbiAKMDAwMDAxOTIwNyAwMDAwMCBuIAowMDAwMDEwNjY1IDAwMDAwIG4gCjAwMDAwMTA0NTggMDAwMDAgbiAKMDAwMDAwOTk4NiAwMDAwMCBuIAowMDAwMDExNzE4IDAwMDAwIG4gCjAwMDAwMDE1MDIgMDAwMDAgbiAKMDAwMDAwMTY1MyAwMDAwMCBuIAowMDAwMDAxODk2IDAwMDAwIG4gCjAwMDAwMDIyNzYgMDAwMDAgbiAKMDAwMDAwMjU4MSAwMDAwMCBuIAowMDAwMDAyODg1IDAwMDAwIG4gCjAwMDAwMDMyMDcgMDAwMDAgbiAKMDAwMDAwMzY3NSAwMDAwMCBuIAowMDAwMDAzOTk3IDAwMDAwIG4gCjAwMDAwMDQxNjMgMDAwMDAgbiAKMDAwMDAwNDU3NyAwMDAwMCBuIAowMDAwMDA0ODE0IDAwMDAwIG4gCjAwMDAwMDQ5NTggMDAwMDAgbiAKMDAwMDAwNTA3NyAwMDAwMCBuIAowMDAwMDA1NDA4IDAwMDAwIG4gCjAwMDAwMDU1ODAgMDAwMDAgbiAKMDAwMDAwNTgxNiAwMDAwMCBuIAowMDAwMDA2MjExIDAwMDAwIG4gCjAwMDAwMDY1MDIgMDAwMDAgbiAKMDAwMDAwNjY1NyAwMDAwMCBuIAowMDAwMDA2NzgwIDAwMDAwIG4gCjAwMDAwMDcwOTYgMDAwMDAgbiAKMDAwMDAwNzMyOSAwMDAwMCBuIAowMDAwMDA3NzM2IDAwMDAwIG4gCjAwMDAwMDc4NzggMDAwMDAgbiAKMDAwMDAwODI3MSAwMDAwMCBuIAowMDAwMDA4MzYxIDAwMDAwIG4gCjAwMDAwMDg1NjcgMDAwMDAgbiAKMDAwMDAwODk4MCAwMDAwMCBuIAowMDAwMDA5MzA0IDAwMDAwIG4gCjAwMDAwMDk1NTEgMDAwMDAgbiAKMDAwMDAwOTY5OCAwMDAwMCBuIAowMDAwMDE5MTg2IDAwMDAwIG4gCjAwMDAwMjAyNjkgMDAwMDAgbiAKMDAwMDAyMDM0OSAwMDAwMCBuIAp0cmFpbGVyCjw8IC9JbmZvIDUzIDAgUiAvUm9vdCAxIDAgUiAvU2l6ZSA1NCA+PgpzdGFydHhyZWYKMjA1MDYKJSVFT0YK\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "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": "e08d9502", "metadata": {"papermill": {"duration": 0.027847, "end_time": "2022-04-09T14:37:38.424774", "exception": false, "start_time": "2022-04-09T14:37:38.396927", "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": "2b42637d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:38.453883Z", "iopub.status.busy": "2022-04-09T14:37:38.453450Z", "iopub.status.idle": "2022-04-09T14:37:39.725682Z", "shell.execute_reply": "2022-04-09T14:37:39.725093Z"}, "papermill": {"duration": 1.289441, "end_time": "2022-04-09T14:37:39.728034", "exception": false, "start_time": "2022-04-09T14:37:38.438593", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "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": "9c2aaea7", "metadata": {"papermill": {"duration": 0.016073, "end_time": "2022-04-09T14:37:39.761079", "exception": false, "start_time": "2022-04-09T14:37:39.745006", "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": "89e4eacc", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.015755, "end_time": "2022-04-09T14:37:39.792307", "exception": false, "start_time": "2022-04-09T14:37:39.776552", "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": "3ff0b95e", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:39.824950Z", "iopub.status.busy": "2022-04-09T14:37:39.824523Z", "iopub.status.idle": "2022-04-09T14:37:39.829883Z", "shell.execute_reply": "2022-04-09T14:37:39.829320Z"}, "papermill": {"duration": 0.023188, "end_time": "2022-04-09T14:37:39.831291", "exception": false, "start_time": "2022-04-09T14:37:39.808103", "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": "4d743412", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:39.863573Z", "iopub.status.busy": "2022-04-09T14:37:39.863112Z", "iopub.status.idle": "2022-04-09T14:37:40.202301Z", "shell.execute_reply": "2022-04-09T14:37:40.201715Z"}, "papermill": {"duration": 0.356913, "end_time": "2022-04-09T14:37:40.203758", "exception": false, "start_time": "2022-04-09T14:37:39.846845", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "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": "c6d66245", "metadata": {"papermill": {"duration": 0.016995, "end_time": "2022-04-09T14:37:40.238619", "exception": false, "start_time": "2022-04-09T14:37:40.221624", "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": "9dbe84c2", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.016824, "end_time": "2022-04-09T14:37:40.272544", "exception": false, "start_time": "2022-04-09T14:37:40.255720", "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": "fba3970c", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.307736Z", "iopub.status.busy": "2022-04-09T14:37:40.307446Z", "iopub.status.idle": "2022-04-09T14:37:40.319189Z", "shell.execute_reply": "2022-04-09T14:37:40.318619Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.031287, "end_time": "2022-04-09T14:37:40.320713", "exception": false, "start_time": "2022-04-09T14:37:40.289426", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class TransformerPredictor(pl.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", " \"\"\"\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": "e2f853d7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017029, "end_time": "2022-04-09T14:37:40.355127", "exception": false, "start_time": "2022-04-09T14:37:40.338098", "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": "678b995e", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.390306Z", "iopub.status.busy": "2022-04-09T14:37:40.389769Z", "iopub.status.idle": "2022-04-09T14:37:40.394655Z", "shell.execute_reply": "2022-04-09T14:37:40.394092Z"}, "papermill": {"duration": 0.023865, "end_time": "2022-04-09T14:37:40.396024", "exception": false, "start_time": "2022-04-09T14:37:40.372159", "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": "d5c57b9a", "metadata": {"papermill": {"duration": 0.017261, "end_time": "2022-04-09T14:37:40.430322", "exception": false, "start_time": "2022-04-09T14:37:40.413061", "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": "7d155c78", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.465826Z", "iopub.status.busy": "2022-04-09T14:37:40.465300Z", "iopub.status.idle": "2022-04-09T14:37:40.483277Z", "shell.execute_reply": "2022-04-09T14:37:40.482703Z"}, "papermill": {"duration": 0.037365, "end_time": "2022-04-09T14:37:40.484763", "exception": false, "start_time": "2022-04-09T14:37:40.447398", "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": "108b3cc8", "metadata": {"papermill": {"duration": 0.017281, "end_time": "2022-04-09T14:37:40.519172", "exception": false, "start_time": "2022-04-09T14:37:40.501891", "status": "completed"}, "tags": []}, "source": ["Let's look at an arbitrary sample of the dataset:"]}, {"cell_type": "code", "execution_count": 17, "id": "54622080", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.554479Z", "iopub.status.busy": "2022-04-09T14:37:40.553970Z", "iopub.status.idle": "2022-04-09T14:37:40.558389Z", "shell.execute_reply": "2022-04-09T14:37:40.557790Z"}, "papermill": {"duration": 0.023559, "end_time": "2022-04-09T14:37:40.559783", "exception": false, "start_time": "2022-04-09T14:37:40.536224", "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": "73604b92", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017109, "end_time": "2022-04-09T14:37:40.594221", "exception": false, "start_time": "2022-04-09T14:37:40.577112", "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": "3157b207", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.629517Z", "iopub.status.busy": "2022-04-09T14:37:40.629034Z", "iopub.status.idle": "2022-04-09T14:37:40.635426Z", "shell.execute_reply": "2022-04-09T14:37:40.634853Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.025464, "end_time": "2022-04-09T14:37:40.636801", "exception": false, "start_time": "2022-04-09T14:37:40.611337", "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": "1eca170b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017272, "end_time": "2022-04-09T14:37:40.673633", "exception": false, "start_time": "2022-04-09T14:37:40.656361", "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 `pl.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": "237c583b", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.709513Z", "iopub.status.busy": "2022-04-09T14:37:40.708933Z", "iopub.status.idle": "2022-04-09T14:37:40.715633Z", "shell.execute_reply": "2022-04-09T14:37:40.715064Z"}, "papermill": {"duration": 0.026011, "end_time": "2022-04-09T14:37:40.717000", "exception": false, "start_time": "2022-04-09T14:37:40.690989", "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 = pl.Trainer(\n", " default_root_dir=root_dir,\n", " callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\")],\n", " gpus=1 if str(device).startswith(\"cuda\") else 0,\n", " max_epochs=10,\n", " gradient_clip_val=5,\n", " progress_bar_refresh_rate=1,\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": "170c6143", "metadata": {"papermill": {"duration": 0.017478, "end_time": "2022-04-09T14:37:40.752397", "exception": false, "start_time": "2022-04-09T14:37:40.734919", "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": "da3e1d49", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:40.788473Z", "iopub.status.busy": "2022-04-09T14:37:40.787980Z", "iopub.status.idle": "2022-04-09T14:37:44.274530Z", "shell.execute_reply": "2022-04-09T14:37:44.273926Z"}, "papermill": {"duration": 3.505945, "end_time": "2022-04-09T14:37:44.275998", "exception": false, "start_time": "2022-04-09T14:37:40.770053", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:96: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=1)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n", " rank_zero_deprecation(\n", "GPU available: True, 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": ["Missing logger folder: saved_models/Transformers/ReverseTask/lightning_logs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 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": "193ce83655fa4e29bf45db53f6479208", "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": "765cb12491a549dba9e5145f9eec3a16", "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": "97c40edb", "metadata": {"papermill": {"duration": 0.018062, "end_time": "2022-04-09T14:37:44.314089", "exception": false, "start_time": "2022-04-09T14:37:44.296027", "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": "979831eb", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:44.351363Z", "iopub.status.busy": "2022-04-09T14:37:44.350923Z", "iopub.status.idle": "2022-04-09T14:37:44.355034Z", "shell.execute_reply": "2022-04-09T14:37:44.354446Z"}, "papermill": {"duration": 0.024528, "end_time": "2022-04-09T14:37:44.356427", "exception": false, "start_time": "2022-04-09T14:37:44.331899", "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": "bbab7649", "metadata": {"papermill": {"duration": 0.017964, "end_time": "2022-04-09T14:37:44.392392", "exception": false, "start_time": "2022-04-09T14:37:44.374428", "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": "c8d2704d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:44.429196Z", "iopub.status.busy": "2022-04-09T14:37:44.428820Z", "iopub.status.idle": "2022-04-09T14:37:44.436248Z", "shell.execute_reply": "2022-04-09T14:37:44.435661Z"}, "papermill": {"duration": 0.027384, "end_time": "2022-04-09T14:37:44.437643", "exception": false, "start_time": "2022-04-09T14:37:44.410259", "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": "a0f1e662", "metadata": {"papermill": {"duration": 0.017949, "end_time": "2022-04-09T14:37:44.473368", "exception": false, "start_time": "2022-04-09T14:37:44.455419", "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": "59364e79", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:44.510010Z", "iopub.status.busy": "2022-04-09T14:37:44.509466Z", "iopub.status.idle": "2022-04-09T14:37:44.514032Z", "shell.execute_reply": "2022-04-09T14:37:44.513456Z"}, "papermill": {"duration": 0.024284, "end_time": "2022-04-09T14:37:44.515442", "exception": false, "start_time": "2022-04-09T14:37:44.491158", "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": "d9ce1e83", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.017929, "end_time": "2022-04-09T14:37:44.551386", "exception": false, "start_time": "2022-04-09T14:37:44.533457", "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": "40c1181d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:44.588212Z", "iopub.status.busy": "2022-04-09T14:37:44.587738Z", "iopub.status.idle": "2022-04-09T14:37:44.595498Z", "shell.execute_reply": "2022-04-09T14:37:44.594933Z"}, "papermill": {"duration": 0.027654, "end_time": "2022-04-09T14:37:44.596896", "exception": false, "start_time": "2022-04-09T14:37:44.569242", "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": "b918b716", "metadata": {"papermill": {"duration": 0.020232, "end_time": "2022-04-09T14:37:44.634993", "exception": false, "start_time": "2022-04-09T14:37:44.614761", "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": "655c60eb", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:44.672583Z", "iopub.status.busy": "2022-04-09T14:37:44.672183Z", "iopub.status.idle": "2022-04-09T14:37:44.958647Z", "shell.execute_reply": "2022-04-09T14:37:44.958104Z"}, "papermill": {"duration": 0.306872, "end_time": "2022-04-09T14:37:44.960097", "exception": false, "start_time": "2022-04-09T14:37:44.653225", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plot_attention_maps(data_input, attention_maps, idx=0)"]}, {"cell_type": "markdown", "id": "9d4769e6", "metadata": {"papermill": {"duration": 0.019048, "end_time": "2022-04-09T14:37:44.998918", "exception": false, "start_time": "2022-04-09T14:37:44.979870", "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": "737558c4", "metadata": {"papermill": {"duration": 0.019071, "end_time": "2022-04-09T14:37:45.039252", "exception": false, "start_time": "2022-04-09T14:37:45.020181", "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": "cc71ab85", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:45.079178Z", "iopub.status.busy": "2022-04-09T14:37:45.078745Z", "iopub.status.idle": "2022-04-09T14:37:50.004285Z", "shell.execute_reply": "2022-04-09T14:37:50.003611Z"}, "papermill": {"duration": 4.947683, "end_time": "2022-04-09T14:37:50.006276", "exception": false, "start_time": "2022-04-09T14:37:45.058593", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to /__w/1/s/.datasets/cifar-100-python.tar.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "8e9c7cfd6f554ff0afc421a3bec240c1", "version_major": 2, "version_minor": 0}, "text/plain": [" 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": "bb6a628d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:37:51.343510Z", "iopub.status.busy": "2022-04-09T14:37:51.343092Z", "iopub.status.idle": "2022-04-09T14:38:23.333962Z", "shell.execute_reply": "2022-04-09T14:38:23.333239Z"}, "papermill": {"duration": 32.013591, "end_time": "2022-04-09T14:38:23.335836", "exception": false, "start_time": "2022-04-09T14:37:51.322245", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f06c41b025b242e0871cd4c0c4344903", "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": "5864ca11", "metadata": {"papermill": {"duration": 0.020134, "end_time": "2022-04-09T14:38:23.818378", "exception": false, "start_time": "2022-04-09T14:38:23.798244", "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": "ed1c9a5d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:23.859404Z", "iopub.status.busy": "2022-04-09T14:38:23.858895Z", "iopub.status.idle": "2022-04-09T14:38:25.413135Z", "shell.execute_reply": "2022-04-09T14:38:25.412472Z"}, "papermill": {"duration": 1.57685, "end_time": "2022-04-09T14:38:25.415045", "exception": false, "start_time": "2022-04-09T14:38:23.838195", "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": "cd49b542", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.019961, "end_time": "2022-04-09T14:38:25.455965", "exception": false, "start_time": "2022-04-09T14:38:25.436004", "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": "b41d7af9", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:25.533543Z", "iopub.status.busy": "2022-04-09T14:38:25.533074Z", "iopub.status.idle": "2022-04-09T14:38:26.723486Z", "shell.execute_reply": "2022-04-09T14:38:26.722841Z"}, "papermill": {"duration": 1.219496, "end_time": "2022-04-09T14:38:26.730572", "exception": false, "start_time": "2022-04-09T14:38:25.511076", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "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": "11b238fe", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.027718, "end_time": "2022-04-09T14:38:26.790695", "exception": false, "start_time": "2022-04-09T14:38:26.762977", "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": "cae76a7d", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:26.846204Z", "iopub.status.busy": "2022-04-09T14:38:26.845694Z", "iopub.status.idle": "2022-04-09T14:38:26.852625Z", "shell.execute_reply": "2022-04-09T14:38:26.852037Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.036334, "end_time": "2022-04-09T14:38:26.853995", "exception": false, "start_time": "2022-04-09T14:38:26.817661", "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": "06d0e531", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.02682, "end_time": "2022-04-09T14:38:26.907606", "exception": false, "start_time": "2022-04-09T14:38:26.880786", "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": "08617179", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:26.962298Z", "iopub.status.busy": "2022-04-09T14:38:26.961898Z", "iopub.status.idle": "2022-04-09T14:38:26.969388Z", "shell.execute_reply": "2022-04-09T14:38:26.968864Z"}, "papermill": {"duration": 0.036406, "end_time": "2022-04-09T14:38:26.970728", "exception": false, "start_time": "2022-04-09T14:38:26.934322", "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 = pl.Trainer(\n", " default_root_dir=root_dir,\n", " callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\")],\n", " gpus=1 if str(device).startswith(\"cuda\") else 0,\n", " max_epochs=100,\n", " gradient_clip_val=2,\n", " progress_bar_refresh_rate=1,\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": "b7b92115", "metadata": {"papermill": {"duration": 0.026792, "end_time": "2022-04-09T14:38:27.025641", "exception": false, "start_time": "2022-04-09T14:38:26.998849", "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": "d9255dd3", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:27.083687Z", "iopub.status.busy": "2022-04-09T14:38:27.083219Z", "iopub.status.idle": "2022-04-09T14:38:33.749385Z", "shell.execute_reply": "2022-04-09T14:38:33.748699Z"}, "papermill": {"duration": 6.698063, "end_time": "2022-04-09T14:38:33.751298", "exception": false, "start_time": "2022-04-09T14:38:27.053235", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:96: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=1)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n", " rank_zero_deprecation(\n", "GPU available: True, 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": ["Missing logger folder: saved_models/Transformers/SetAnomalyTask/lightning_logs\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": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:486: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\n", " rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0625431459604399988364799dca2df9", "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": "b04bc845211544c89614209753f99350", "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": "19a4c24b35d141beaee60fd6943a314f", "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": "864122d1", "metadata": {"papermill": {"duration": 0.02784, "end_time": "2022-04-09T14:38:33.809205", "exception": false, "start_time": "2022-04-09T14:38:33.781365", "status": "completed"}, "tags": []}, "source": ["We can print the achieved accuracy below."]}, {"cell_type": "code", "execution_count": 37, "id": "b175ebcf", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:33.867087Z", "iopub.status.busy": "2022-04-09T14:38:33.866747Z", "iopub.status.idle": "2022-04-09T14:38:33.871557Z", "shell.execute_reply": "2022-04-09T14:38:33.870965Z"}, "papermill": {"duration": 0.035892, "end_time": "2022-04-09T14:38:33.873002", "exception": false, "start_time": "2022-04-09T14:38:33.837110", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Train accuracy: 96.33%\n", "Val accuracy: 95.92%\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": "4597613b", "metadata": {"papermill": {"duration": 0.028746, "end_time": "2022-04-09T14:38:33.929965", "exception": false, "start_time": "2022-04-09T14:38:33.901219", "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": "e21d7c19", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:33.989528Z", "iopub.status.busy": "2022-04-09T14:38:33.988920Z", "iopub.status.idle": "2022-04-09T14:38:34.158814Z", "shell.execute_reply": "2022-04-09T14:38:34.158130Z"}, "papermill": {"duration": 0.202213, "end_time": "2022-04-09T14:38:34.160312", "exception": false, "start_time": "2022-04-09T14:38:33.958099", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Preds\n", " [2.7690839e-05 1.8979506e-05 1.7386024e-05 2.7842490e-05 1.6142623e-05\n", " 1.7020535e-05 5.7293695e-05 9.9977750e-01 2.1364667e-05 1.8681461e-05]\n", "Permuted preds\n", " [2.7690839e-05 1.8979506e-05 1.7386024e-05 2.7842490e-05 1.6142623e-05\n", " 1.7020551e-05 5.7293695e-05 9.9977750e-01 2.1364667e-05 1.8681461e-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": "cfa3f105", "metadata": {"papermill": {"duration": 0.027842, "end_time": "2022-04-09T14:38:34.218704", "exception": false, "start_time": "2022-04-09T14:38:34.190862", "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": "f18c4b3b", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:34.275795Z", "iopub.status.busy": "2022-04-09T14:38:34.275468Z", "iopub.status.idle": "2022-04-09T14:38:34.284821Z", "shell.execute_reply": "2022-04-09T14:38:34.284236Z"}, "papermill": {"duration": 0.039607, "end_time": "2022-04-09T14:38:34.286253", "exception": false, "start_time": "2022-04-09T14:38:34.246646", "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": "89501efb", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.031596, "end_time": "2022-04-09T14:38:34.345543", "exception": false, "start_time": "2022-04-09T14:38:34.313947", "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": "0ac60ff9", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:34.403398Z", "iopub.status.busy": "2022-04-09T14:38:34.402959Z", "iopub.status.idle": "2022-04-09T14:38:37.403708Z", "shell.execute_reply": "2022-04-09T14:38:37.403114Z"}, "papermill": {"duration": 3.032796, "end_time": "2022-04-09T14:38:37.406875", "exception": false, "start_time": "2022-04-09T14:38:34.374079", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Prediction: 9\n"]}, {"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "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": "f091ab42", "metadata": {"papermill": {"duration": 0.036027, "end_time": "2022-04-09T14:38:37.481386", "exception": false, "start_time": "2022-04-09T14:38:37.445359", "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": "dc531905", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:37.555197Z", "iopub.status.busy": "2022-04-09T14:38:37.554672Z", "iopub.status.idle": "2022-04-09T14:38:37.559467Z", "shell.execute_reply": "2022-04-09T14:38:37.558879Z"}, "papermill": {"duration": 0.043753, "end_time": "2022-04-09T14:38:37.560941", "exception": false, "start_time": "2022-04-09T14:38:37.517188", "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": "a0e65068", "metadata": {"papermill": {"duration": 0.035421, "end_time": "2022-04-09T14:38:37.632810", "exception": false, "start_time": "2022-04-09T14:38:37.597389", "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": "dcc6dcbe", "metadata": {"execution": {"iopub.execute_input": "2022-04-09T14:38:37.710200Z", "iopub.status.busy": "2022-04-09T14:38:37.709572Z", "iopub.status.idle": "2022-04-09T14:38:40.661953Z", "shell.execute_reply": "2022-04-09T14:38:40.661355Z"}, "papermill": {"duration": 2.996681, "end_time": "2022-04-09T14:38:40.665341", "exception": false, "start_time": "2022-04-09T14:38:37.668660", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Prediction: 7\n"]}, {"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "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: 23.27%\n", "Image 6: 0.16%\n", "Image 7: 48.91%\n", "Image 8: 0.10%\n", "Image 9: 27.03%\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": "67bedc66", "metadata": {"papermill": {"duration": 0.044306, "end_time": "2022-04-09T14:38:40.755550", "exception": false, "start_time": "2022-04-09T14:38:40.711244", "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": "c1c016b9", "metadata": {"papermill": {"duration": 0.042832, "end_time": "2022-04-09T14:38:40.841468", "exception": false, "start_time": "2022-04-09T14:38:40.798636", "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": "7d3ce98b", "metadata": {"papermill": {"duration": 0.04299, "end_time": "2022-04-09T14:38:40.927714", "exception": false, "start_time": "2022-04-09T14:38:40.884724", "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/PyTorchLightning/pytorch-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://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)!\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/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/PyTorchLightning/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", "[{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": "id,colab,colab_type,-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": "3.9.12"}, "papermill": {"default_parameters": {}, "duration": 72.589227, "end_time": "2022-04-09T14:38:41.893966", "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": "2022-04-09T14:37:29.304739", "version": "2.3.4"}, "widgets": 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