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                <h1 id="embedding">Embedding量化示例<a class="headerlink" href="#embedding" title="Permanent link">#</a></h1>
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<p>本示例介绍如何使用Embedding量化的接口 <a href="">paddleslim.quant.quant_embedding</a> 。<code>quant_embedding</code>接口将网络中的Embedding参数从<code>float32</code>类型量化到 <code>8-bit</code>整数类型，在几乎不损失模型精度的情况下减少模型的存储空间和显存占用。</p>
<p>接口介绍请参考 <a href='../../../paddleslim/quant/quantization_api_doc.md'>量化API文档</a>。</p>
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<p>该接口对program的修改：</p>
<p>量化前:</p>
<p align="center">
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<img src="./image/before.png" height=200 width=100 hspace='10'/> <br />
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<strong>图1：量化前的模型结构</strong>
</p>

<p>量化后：</p>
<p align="center">
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<img src="./image/after.png" height=300 width=300 hspace='10'/> <br />
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<strong>图2: 量化后的模型结构</strong>
</p>

<p>以下将以 <code>基于skip-gram的word2vector模型</code> 为例来说明如何使用<code>quant_embedding</code>接口。首先介绍 <code>基于skip-gram的word2vector模型</code> 的正常训练和测试流程。</p>
<h2 id="skip-gramword2vector">基于skip-gram的word2vector模型<a class="headerlink" href="#skip-gramword2vector" title="Permanent link">#</a></h2>
<p>以下是本例的简要目录结构及说明：</p>
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<div class="codehilite"><pre><span></span>.
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├── cluster_train.py    # 分布式训练函数
├── cluster_train.sh    # 本地模拟多机脚本
├── train.py            # 训练函数
├── infer.py            # 预测脚本
├── net.py              # 网络结构
├── preprocess.py       # 预处理脚本，包括构建词典和预处理文本
├── reader.py           # 训练阶段的文本读写
├── train.py            # 训练函数
└── utils.py            # 通用函数
</pre></div>

<h3 id="_1">介绍<a class="headerlink" href="#_1" title="Permanent link">#</a></h3>
<p>本例实现了skip-gram模式的word2vector模型。</p>
<p>同时推荐用户参考<a href="https://aistudio.baidu.com/aistudio/projectDetail/124377"> IPython Notebook demo</a></p>
<h3 id="_2">数据下载<a class="headerlink" href="#_2" title="Permanent link">#</a></h3>
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<p>全量数据集使用的是来自1 Billion Word Language Model Benchmark的(<a href="http://www.statmt.org/lm-benchmark">http://www.statmt.org/lm-benchmark</a>) 的数据集.</p>
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<div class="codehilite"><pre><span></span>mkdir data
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wget http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz
tar xzvf <span class="m">1</span>-billion-word-language-modeling-benchmark-r13output.tar.gz
mv <span class="m">1</span>-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/
</pre></div>

<p>备用数据地址下载命令如下</p>
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<div class="codehilite"><pre><span></span>mkdir data
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wget https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar
tar xvf <span class="m">1</span>-billion-word-language-modeling-benchmark-r13output.tar
mv <span class="m">1</span>-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/ data/
</pre></div>

<p>为了方便快速验证，我们也提供了经典的text8样例数据集，包含1700w个词。 下载命令如下</p>
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<div class="codehilite"><pre><span></span>mkdir data
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wget https://paddlerec.bj.bcebos.com/word2vec/text.tar
tar xvf text.tar
mv text data/
</pre></div>

<h3 id="_3">数据预处理<a class="headerlink" href="#_3" title="Permanent link">#</a></h3>
<p>以样例数据集为例进行预处理。全量数据集注意解压后以training-monolingual.tokenized.shuffled 目录为预处理目录，和样例数据集的text目录并列。</p>
<p>词典格式: 词&lt;空格&gt;词频。注意低频词用'UNK'表示</p>
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<p>可以按格式自建词典，如果自建词典跳过第一步。
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<div class="codehilite"><pre><span></span>the 1061396
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of 593677
and 416629
one 411764
in 372201
a 325873
&lt;UNK&gt; 324608
to 316376
zero 264975
nine 250430
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</pre></div></p>
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<p>第一步根据英文语料生成词典，中文语料可以通过修改text_strip方法自定义处理方法。</p>
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<div class="codehilite"><pre><span></span>python preprocess.py --build_dict --build_dict_corpus_dir data/text/ --dict_path data/test_build_dict
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</pre></div>

<p>第二步根据词典将文本转成id, 同时进行downsample，按照概率过滤常见词, 同时生成word和id映射的文件，文件名为词典+"<em>word_to_id</em>"。</p>
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<div class="codehilite"><pre><span></span>python preprocess.py --filter_corpus --dict_path data/test_build_dict --input_corpus_dir data/text --output_corpus_dir data/convert_text8 --min_count <span class="m">5</span> --downsample <span class="m">0</span>.001
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</pre></div>

<h3 id="_4">训练<a class="headerlink" href="#_4" title="Permanent link">#</a></h3>
<p>具体的参数配置可运行</p>
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<div class="codehilite"><pre><span></span>python train.py -h
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</pre></div>
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<p>单机多线程训练
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<div class="codehilite"><pre><span></span><span class="nv">OPENBLAS_NUM_THREADS</span><span class="o">=</span><span class="m">1</span> <span class="nv">CPU_NUM</span><span class="o">=</span><span class="m">5</span> python train.py --train_data_dir data/convert_text8 --dict_path data/test_build_dict --num_passes <span class="m">10</span> --batch_size <span class="m">100</span> --model_output_dir v1_cpu5_b100_lr1dir --base_lr <span class="m">1</span>.0 --print_batch <span class="m">1000</span> --with_speed --is_sparse
</pre></div></p>
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<p>本地单机模拟多机训练</p>
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<div class="codehilite"><pre><span></span>sh cluster_train.sh
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</pre></div>
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<p>本示例中按照单机多线程训练的命令进行训练，训练完毕后，可看到在当前文件夹下保存模型的路径为:     <code>v1_cpu5_b100_lr1dir</code>, 运行 <code>ls v1_cpu5_b100_lr1dir</code>可看到该文件夹下保存了训练的10个epoch的模型文件。
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<div class="codehilite"><pre><span></span>pass-0  pass-1  pass-2  pass-3  pass-4  pass-5  pass-6  pass-7  pass-8  pass-9
</pre></div></p>
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<h3 id="_5">预测<a class="headerlink" href="#_5" title="Permanent link">#</a></h3>
<p>测试集下载命令如下</p>
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<div class="codehilite"><pre><span></span><span class="c1">#全量数据集测试集</span>
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wget https://paddlerec.bj.bcebos.com/word2vec/test_dir.tar
<span class="c1">#样本数据集测试集</span>
wget https://paddlerec.bj.bcebos.com/word2vec/test_mid_dir.tar
</pre></div>
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<p>预测命令，注意词典名称需要加后缀"<em>word_to_id</em>", 此文件是预处理阶段生成的。
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<div class="codehilite"><pre><span></span>python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size <span class="m">20000</span> --model_dir v1_cpu5_b100_lr1dir/  --start_index <span class="m">0</span> --last_index <span class="m">9</span>
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</pre></div>
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运行该预测命令, 可看到如下输出
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<div class="codehilite"><pre><span></span>(&#39;start index: &#39;, 0, &#39; last_index:&#39;, 9)
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(&#39;vocab_size:&#39;, 63642)
step:1 249
epoch:0          acc:0.014
step:1 590
epoch:1          acc:0.033
step:1 982
epoch:2          acc:0.055
step:1 1338
epoch:3          acc:0.075
step:1 1653
epoch:4          acc:0.093
step:1 1914
epoch:5          acc:0.107
step:1 2204
epoch:6          acc:0.124
step:1 2416
epoch:7          acc:0.136
step:1 2606
epoch:8          acc:0.146
step:1 2722
epoch:9          acc:0.153
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</pre></div></p>
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<h2 id="skip-gramword2vector_1">量化<code>基于skip-gram的word2vector模型</code><a class="headerlink" href="#skip-gramword2vector_1" title="Permanent link">#</a></h2>
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<p>量化配置为:
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<div class="codehilite"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="p">{</span>
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        <span class="s1">&#39;params_name&#39;</span><span class="p">:</span> <span class="s1">&#39;emb&#39;</span><span class="p">,</span>
        <span class="s1">&#39;quantize_type&#39;</span><span class="p">:</span> <span class="s1">&#39;abs_max&#39;</span>
        <span class="p">}</span>
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</pre></div></p>
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<p>运行命令为：</p>
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<div class="codehilite"><pre><span></span>python infer.py --infer_epoch --test_dir data/test_mid_dir --dict_path data/test_build_dict_word_to_id_ --batch_size <span class="m">20000</span> --model_dir v1_cpu5_b100_lr1dir/  --start_index <span class="m">0</span> --last_index <span class="m">9</span> --emb_quant True
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</pre></div>

<p>运行输出为:</p>
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<div class="codehilite"><pre><span></span>(&#39;start index: &#39;, 0, &#39; last_index:&#39;, 9)
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(&#39;vocab_size:&#39;, 63642)
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 253
epoch:0          acc:0.014
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 586
epoch:1          acc:0.033
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 970
epoch:2          acc:0.054
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 1364
epoch:3          acc:0.077
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 1642
epoch:4          acc:0.092
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 1936
epoch:5          acc:0.109
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 2216
epoch:6          acc:0.124
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 2419
epoch:7          acc:0.136
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 2603
epoch:8          acc:0.146
quant_embedding config {&#39;quantize_type&#39;: &#39;abs_max&#39;, &#39;params_name&#39;: &#39;emb&#39;, &#39;quantize_bits&#39;: 8, &#39;dtype&#39;: &#39;int8&#39;}
step:1 2719
epoch:9          acc:0.153
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</pre></div>

<p>量化后的模型保存在<code>./output_quant</code>中，可看到量化后的参数<code>'emb.int8'</code>的大小为3.9M, 在<code>./v1_cpu5_b100_lr1dir</code>中可看到量化前的参数<code>'emb'</code>的大小为16M。</p>
              
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