RN-08516-001_v23.07 | July 2023 PyTorch Release Notes PyTorch RN-08516-001_v23.07 | ii Table of Contents Chapter 1. PyTorch Overview......................................................... 515.65 (or later R515), 525.85 (or later R525), or 530.30 (or later R530). The CUDA driver's compatibility package only supports particular drivers. Thus, users should upgrade from all R418, R440, R460 For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements Release 23.07 supports CUDA compute

animals. The higher the value, the more that particular feature represents the given animal. In Table 4-1 we manually assigned values for the cute and dangerous features for six animals2, and we are calling cat (0.95, 0.05) snake (0.01, 0.9) bear (0.5, 0.95) raccoon (0.5, 0.5) mouse (0.01, 0.2) Table 4-1: A table consisting of embeddings of the various animals, using two features (cute and dangerous), each take a value between 0.0 and 1.0. We manually picked these values for illustration. Going through table 4-1, cat and dog have high values for the ‘cute’ feature, and low values for the ‘dangerous’ feature

familiarize ourselves with these techniques later in this chapter. Table 3-1 presents a concise summary of both, sample and label efficiency. Table 3-1: A quick summary of sample and label efficiencies. Both Using learning techniques to build smaller and faster efficient models Overall, as summarized in table 3-1, improving sample efficiency enables faster model training, and label efficiency is useful to scenario which illustrates how learning techniques are leveraged to reduce the model footprint. Table 3-2 shows a comparison of vanilla models (without the learning techniques) with the models that employ

org/licenses/by-sa/4.0/deed.en Data Volume © 2018 Bloomberg Finance L.P. All rights reserved. Back in 2016 – Table Extraction © 2018 Bloomberg Finance L.P. All rights reserved. Tables Look Different © 2018 Bloomberg reserved. Table Detection – How Do We Do It © 2018 Bloomberg Finance L.P. All rights reserved. Table Detection – How Do We Do It © 2018 Bloomberg Finance L.P. All rights reserved. Table Detection – https://creativecommons.org/licenses/by-sa/4.0/deed.en © 2018 Bloomberg Finance L.P. All rights reserved. Table Detection Is Object Detection Deep learning has yielded rapid advancements in computer vision ©

参数更新 查询Sparse Table 查询Dense Tensor Reader Learner Worker 返回参数 Request Handler Parameter Server 查询Sparse Table 查询Dense Tensor 更新参数 � 常规训练流⽔线 样本读取 样本解析 参数拉取 参数更新 查询Sparse Table 查询Dense Tensor 训练任务 成本 内存是主要瓶颈 > Embedding table可以设计得更⼩么？Double Hashing Embedding Table与第⼀层fc可以看作低秩矩阵分解 亿 亿 512 512 9 9 原始矩阵 矩阵分解 压缩⼿段除了量化和稀疏化，还有什么？因式分解 问题： 1. Embedding Table的信息仍然⾮常稀疏 2. 直接压缩key空间和缩⼩value⻓度都会导致模型效果下降

aggregate, this would be better than encoding each symbol with the same number of bits. The lookup table (figure 2-1 middle) that contains the symbol-code mapping is transmitted along with the encoded data Encoding & Huffman Tree. Source When decoding the encoded data, we look up the code from the lookup table to retrieve the symbols back. Since the codes are unique for each symbol (in fact, they are prefix we can easily construct the original sequence of symbols from the encoded sequence and the lookup table. Refer the wikipedia article on arithmetic coding to learn about lossless coding schemes. The lossy

r is smaller and vice-versa to ensure that each bracket gets a comparable budget. Take a look at table 7-1 which shows the changes in the number of configurations as the iterations progress for each bracket and statistics. PMLR, 2016. 1 27, 3 9, 9 3, 27 2, 81 2 9, 9 3, 27 1, 81 3 3, 27 1, 81 4 1, 81 Table 7-1: A demonstration of configuration and resource allocation changes across multiple brackets in achieves a better accuracy as well. Table 7-2 shows a breakdown of trials for this run. Note that the bracket ids are in reverse order in contrast to the example in table 7-1. The tuner runs a total of 30

elements is . Assuming , and as usual, the compression ratio using the above formula computes to: . Table 5-1 lists the compression ratios for different values of , using the above values of and . Number between the original tensor and the decoded tensor, as we did when working with quantization. 128 4.0 Table 5-1: Number of centroids v/s the compression ratio, assuming , and . As we increase the value of forward pass for them is a simple lookup in the table using the provided index. The embeddings themselves can be saved in an optimized format. Either the table in its entirety can be converted back to the

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6.1.4 make_sampling_table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.2 文本预处理 . . . . . . . . . . . vocabulary_size,window_size=4, negative_samples=1.0, shuffle=True, categorical=False, sampling_table=None, seed=None) 生成 skipgram 词对。 该函数将一个单词索引序列（整数列表）转化为以下形式的单词元组： •（单词, 同窗口的单词），标签为 1（正样本）。 •（单词 of Word Representations in Vector Space 参数 • sequence: 一个编码为单词索引（整数）列表的词序列（句子） 。如果使用一个 sampling_table，词索引应该以一个相关数据集的词的排名匹配（例如，10 将会编码为 第 10 个最长出现的词）。注意词汇表中的索引 0 是非单词，将被跳过。 • vocabulary_size: 整数，最大可能词索引

vocab,?) Embedding 层实现起来非常简单，构建一个 shape 为[?vocab,?]的查询表对象 table，对 预览版202112 11.1 序列表示方法 3 于任意的单词编号?，只需要查询到对应位置上的向量并返回即可： ? = table[?] Embedding 层是可训练的，它可放置在神经网络之前，完成单词到向量的转换，得到的表 示向量可以继续 层内部的查询表 table： In [1]: # 打印内部查询表张量 for name,p in net.named_parameters(): print(name, p.shape) print('table:', next(net.parameters())) Out[1]: weight torch.Size([10, 4]) table: Parameter images, labels, table = load_pokemon('pokemon', 'train') print('images:', len(images), images) print('labels:', len(labels), labels) print('table:', table) 构建 Dataset 对象，并完成数