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            <h1 id="seo-header">【探索】6G显存畅玩无限长度的LLM角色扮演</h1>
            
              <p id="updated-time" class="note note-info" style="">
                
                  
                    Last updated on March 19, 2024 pm
                  
                
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              <div class="markdown-body">
                
                <p>角色扮演的体验是否舒适主要受角色卡、大模型和生成时间三个因素的影响。</p>
<p>优秀的角色卡往往附带大量的设定，这会极大的拖慢第一次生成的时间，并且随着对话的进行，上下文长度很容易超过kv_cache的上限，这些很破坏沉浸式的体验。</p>
<p>此外，大模型在进行角色扮演时，除了进行必要的对话生成外，还需要生成旁白增加想象空间。</p>
<p>对博主这些相比填空更喜欢选项的玩家，给出提问建议也是非常必要的：在建议的基础上修改比自己从零写一个情景更简单，同时也完整保留了控制剧情走向的权力。</p>
<p>以上这些都让本就稀缺的kv_cache更加雪上加霜。</p>
<p>万幸，StreamingLLM 发现了kv_cache具有良好的平移性，而 llama.cpp 也提供了对kv_cache进行底层操作的api：可以指定范围的 kv_cache_seq_rm 和 kv_cache_seq_shift。基于这两个api，我们将实现对kv_cache的 token 级微操，榨干kv_cache的全部价值。</p>
<p>博主实践表明，在充分利用kv_cache的基础上，哪怕是 huggingface space 免费的2vCPU容器也可以游玩角色扮演，而笔记本端6G显存的1660Ti可以做到畅玩角色扮演。</p>
<h2 id="体验-DEMO">体验 DEMO</h2>
<ul>
<li><a href="https://hexo.limour.top/go/#aHR0cHM6Ly9odWdnaW5nZmFjZS5jby9zcGFjZXMvTGltb3VyL2xsYW1hLXB5dGhvbi1zdHJlYW1pbmdsbG0=" rel="noopener external nofollow noreferrer">Limour/llama-python-streamingllm</a></li>
<li>同一时间仅支持一个人用，用之前点 Reset 按钮恢复初始的 kv_cache</li>
<li>按 Submit 没反应，说明有人在用，等一段时间后再 Reset</li>
<li>最好是 Duplicate 后，设为私密来使用</li>
</ul>
<h2 id="代码仓库">代码仓库</h2>
<ul>
<li><a href="https://hexo.limour.top/go/#aHR0cHM6Ly9naXRodWIuY29tL0xpbW91ci1kZXYvbGxhbWEtcHl0aG9uLXN0cmVhbWluZ2xsbQ==" rel="noopener external nofollow noreferrer">llama-python-streamingllm</a></li>
<li><a href="/-ji-lu--an-zhuang-conda-bing-geng-huan-qing-hua-yuan">安装conda</a></li>
<li><a href="/Use-Tunnel-to-speed-up-the-connection-of-VPS">学术上网</a>(管理员权限)</li>
<li>使用前需要修改 <code>rp_config.json</code> 里的模型路径和参数，指定为你已经下载了的<code>GGUF</code>格式模型的路径</li>
<li>推荐 <a href="https://hexo.limour.top/go/#aHR0cHM6Ly9odWdnaW5nZmFjZS5jby9UaGVCbG9rZS9DYXVzYWxMTS03Qi1HR1VGL2Jsb2IvbWFpbi9jYXVzYWxsbV83Yi5RNV9LX00uZ2d1Zg==" rel="noopener external nofollow noreferrer">causallm_7b.Q5_K_M.gguf</a></li>
<li>或者自己用 <a href="https://hexo.limour.top/go/#aHR0cHM6Ly9odWdnaW5nZmFjZS5jby9kYXRhc2V0cy9MaW1vdXIvYi1jb3JwdXM=" rel="noopener external nofollow noreferrer">Galgame</a> 解包的对话数据集微调一个合适的模型。</li>
</ul>
<h3 id="二选一：GPU版本的环境">二选一：GPU版本的环境</h3>
<figure class="highlight powershell"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><code class="hljs powershell">conda create <span class="hljs-literal">-n</span> llamaCpp libcublas cuda<span class="hljs-literal">-toolkit</span> git <span class="hljs-literal">-c</span> nvidia <span class="hljs-literal">-c</span> conda<span class="hljs-literal">-forge</span><br>conda activate llamaCpp<br>conda install python=<span class="hljs-number">3.10</span> gradio <span class="hljs-literal">-c</span> conda<span class="hljs-literal">-forge</span><br><span class="hljs-comment"># 然后去 release 下载相应的包 https://github.com/Limour-dev/llama-cpp-python-cuBLAS-wheels/releases</span><br>pip install <span class="hljs-literal">--force-reinstall</span> llama_cpp_python<span class="hljs-literal">-0</span>.<span class="hljs-number">2.39</span>+cu122<span class="hljs-literal">-cp310-cp310-win_amd64</span>.whl<br></code></pre></td></tr></table></figure>
<h3 id="二选一：CPU版本的环境">二选一：CPU版本的环境</h3>
<figure class="highlight powershell"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><code class="hljs powershell">conda create <span class="hljs-literal">-n</span> llamaCpp python=<span class="hljs-number">3.10</span> gradio git <span class="hljs-literal">-c</span> conda<span class="hljs-literal">-forge</span><br>conda activate llamaCpp<br>pip install llama<span class="hljs-literal">-cpp-python</span>==<span class="hljs-number">0.2</span>.<span class="hljs-number">39</span><br></code></pre></td></tr></table></figure>
<h3 id="下载并运行">下载并运行</h3>
<figure class="highlight powershell"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><code class="hljs powershell">conda activate llamaCpp<br>git clone <span class="hljs-literal">--depth</span>=<span class="hljs-number">1</span> https://github.com/Limour<span class="hljs-literal">-dev</span>/llama<span class="hljs-literal">-python-streamingllm</span>.git<br><span class="hljs-built_in">cd</span> llama<span class="hljs-literal">-python-streamingllm</span><br>mkdir cache<br>python .\gradio_streamingllm.py<br></code></pre></td></tr></table></figure>
<h2 id="核心内容">核心内容</h2>
<ul>
<li><code>Submit</code> 会将 msg 发送给模型，然后流式生成回答</li>
<li><code>Retry</code> 会重新生成最近一次的 msg 所对应的回答</li>
<li><code>旁白</code> 会流式生成一份旁白到 <code>VO</code> 框</li>
<li><code>建议</code> 会以 usr 的身份流式生成一份 msg 供修改</li>
<li>上面四个功能的基础就是下面的基于 StreamingLLM 原理的 venv 开头的函数</li>
</ul>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br></pre></td><td class="code"><pre><code class="hljs python"><span class="hljs-keyword">class</span> <span class="hljs-title class_">StreamingLLM</span>(<span class="hljs-title class_ inherited__">Llama</span>):<br>    <span class="hljs-keyword">pass</span><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">kv_cache_seq_trim</span>(<span class="hljs-params">self</span>):<br>        self._ctx.kv_cache_seq_rm(-<span class="hljs-number">1</span>, self.n_tokens, -<span class="hljs-number">1</span>)<br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">kv_cache_seq_ltrim</span>(<span class="hljs-params">self, n_keep, n_discard=<span class="hljs-number">256</span>, n_past=-<span class="hljs-number">1</span></span>):<br>        <span class="hljs-keyword">if</span> n_past &lt; <span class="hljs-number">0</span>:<br>            n_past = self.n_tokens<br>        self._ctx.kv_cache_seq_rm(-<span class="hljs-number">1</span>, n_keep, n_keep + n_discard)<br>        self._ctx.kv_cache_seq_shift(<span class="hljs-number">0</span>, n_keep + n_discard, n_past, -n_discard)<br>        self.input_ids[n_keep:n_past - n_discard] = self.input_ids[n_keep + n_discard:n_past]<br>        self.n_tokens = n_past - n_discard<br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">_venv_init</span>(<span class="hljs-params">self</span>):<br>        self.venv = [<span class="hljs-number">0</span>]<br>        self.venv_idx_map = []<br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">venv_create</span>(<span class="hljs-params">self, name: <span class="hljs-built_in">str</span></span>):<br>        self.venv.append(<span class="hljs-number">0</span>)<br>        self.venv_idx_map.append(name)<br>        <span class="hljs-keyword">return</span> name<br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">venv_disband</span>(<span class="hljs-params">self, name_set</span>):<br>        <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(self.venv) &lt;= <span class="hljs-number">1</span>:<br>            <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span><br>        name_set = &#123;x <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> name_set <span class="hljs-keyword">if</span> x <span class="hljs-keyword">in</span> self.venv_idx_map&#125;<br>        <span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> name_set:<br>            <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span><br>        <span class="hljs-keyword">while</span> self.venv_idx_map:<br>            <span class="hljs-keyword">if</span> self.venv_idx_map[<span class="hljs-number">0</span>] <span class="hljs-keyword">in</span> name_set:<br>                self.venv_idx_map.pop(<span class="hljs-number">0</span>)  <span class="hljs-comment"># 删除</span><br>                tmp = self.venv.pop(<span class="hljs-number">1</span>)  <span class="hljs-comment"># 对应的 venv 移入上一层</span><br>                self.venv[<span class="hljs-number">0</span>] += tmp<br>            <span class="hljs-keyword">else</span>:<br>                <span class="hljs-keyword">break</span><br>        <span class="hljs-keyword">return</span> <span class="hljs-literal">True</span><br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">venv_revision</span>(<span class="hljs-params">self, name: <span class="hljs-built_in">str</span></span>):<br>        <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(self.venv) &lt;= <span class="hljs-number">1</span>:<br>            <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span><br>        <span class="hljs-keyword">if</span> name <span class="hljs-keyword">not</span> <span class="hljs-keyword">in</span> self.venv_idx_map:<br>            <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span><br>        _s = <span class="hljs-number">0</span><br>        <span class="hljs-keyword">while</span> self.venv_idx_map:<br>            <span class="hljs-keyword">if</span> self.venv_idx_map[-<span class="hljs-number">1</span>] == name:<br>                <span class="hljs-keyword">break</span><br>            self.venv_idx_map.pop()  <span class="hljs-comment"># 删除</span><br>            _s += self.venv.pop()<br>        <span class="hljs-keyword">if</span> _s:<br>            self.n_tokens -= <span class="hljs-built_in">min</span>(_s, self.n_tokens)<br>            self.kv_cache_seq_trim()<br>        <span class="hljs-keyword">return</span> <span class="hljs-literal">True</span><br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">venv_remove</span>(<span class="hljs-params">self, name: <span class="hljs-built_in">str</span></span>):<br>        <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(self.venv) &lt;= <span class="hljs-number">1</span>:<br>            <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span><br>        <span class="hljs-keyword">if</span> name <span class="hljs-keyword">not</span> <span class="hljs-keyword">in</span> self.venv_idx_map:<br>            <span class="hljs-keyword">return</span> <span class="hljs-literal">False</span><br>        venv_idx = self.venv_idx_map.index(name) + <span class="hljs-number">1</span><br>        <span class="hljs-keyword">while</span> self.venv_idx_map:<br>            self.venv_idx_map.pop(venv_idx - <span class="hljs-number">1</span>)  <span class="hljs-comment"># 删除</span><br>            <span class="hljs-keyword">if</span> venv_idx == <span class="hljs-built_in">len</span>(self.venv) - <span class="hljs-number">1</span>:<br>                <span class="hljs-comment"># 最后一层</span><br>                self.n_tokens -= <span class="hljs-built_in">min</span>(self.venv.pop(), self.n_tokens)<br>                self.kv_cache_seq_trim()<br>                <span class="hljs-keyword">break</span><br>            <span class="hljs-keyword">else</span>:<br>                <span class="hljs-comment"># 非最后一层</span><br>                n_keep = self.n_tokens - <span class="hljs-built_in">sum</span>(self.venv[i] <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(venv_idx, <span class="hljs-built_in">len</span>(self.venv)))<br>                n_discard = self.venv.pop(venv_idx)<br>                self.kv_cache_seq_ltrim(n_keep, n_discard)<br>                <span class="hljs-keyword">try</span>:<br>                    venv_idx = self.venv_idx_map.index(name, venv_idx - <span class="hljs-number">1</span>) + <span class="hljs-number">1</span><br>                <span class="hljs-keyword">except</span> ValueError:  <span class="hljs-comment"># 没有了</span><br>                    <span class="hljs-keyword">break</span><br>        <span class="hljs-keyword">return</span> <span class="hljs-literal">True</span><br><br>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">eval_t</span>(<span class="hljs-params">self, tokens, n_keep=<span class="hljs-number">4</span>, n_discard=<span class="hljs-number">256</span>, im_start=<span class="hljs-literal">None</span></span>):<br>        <span class="hljs-keyword">if</span> self._n_ctx &lt; self.n_tokens + <span class="hljs-built_in">len</span>(tokens):<br>            tmp_n_discard = <span class="hljs-built_in">max</span>(n_discard, self.n_tokens + <span class="hljs-built_in">len</span>(tokens) - self._n_ctx)<br>            self.kv_cache_seq_ltrim(n_keep, tmp_n_discard)<br>        <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(<span class="hljs-number">0</span>, <span class="hljs-built_in">len</span>(tokens), self.n_batch):<br>            <span class="hljs-keyword">pass</span><br>            self.n_tokens += n_tokens<br>            self.venv[-<span class="hljs-number">1</span>] += n_tokens<br></code></pre></td></tr></table></figure>
                
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