add llama.cpp (#51)

* add llama.cpp * add llama_cpp.png * add reasoning result Co-authored-by: sjh <[email protected]>
Ascend · Oct 16, 2024 · 79b3369 · 79b3369
1 parent 5cb3a99
commit 79b3369
Show file tree

Hide file tree

Showing 6 changed files with 382 additions and 0 deletions.
diff --git a/_static/images/llama_cpp.png b/_static/images/llama_cpp.png
diff --git a/_static/llamacpp_actions.js b/_static/llamacpp_actions.js
@@ -0,0 +1,49 @@
+$(document).ready(function () {
+    $.reset_selection = function (elem) {
+        elem.parent().children().each(function () {
+            $(this).removeClass("selected");
+        });
+    }
+
+    $.get_options = function () {
+        var options = {};
+        $('#col-values').children().each(function () {
+            var elem = $(this).find(".selected").each(function () {
+                var id = $(this).attr("id").split("-");
+                var category = id[0];
+                var value = id[1];
+                options[category] = value;
+            });
+        });
+        return options;
+    }
+
+    $.update_table = function () {
+        var options = $.get_options();
+    }
+
+    $("#col-values").on("click", ".values-element", function () {
+        id = $(this).attr("id");
+        fields = id.split("-");
+
+        $.reset_selection($(this));
+        $(this).addClass("selected");
+        $.update_table();
+        $.gen_content();
+    });
+
+
+    $.gen_content = function () {
+        var options = $.get_options();
+        if (options['install_type'] == "docker") {
+            $('#install-llamacpp-pip-section').hide();
+            $('#install-llamacpp-docker-section').show();
+        } else if (options['install_type'] == "pip") {
+            $('#install-llamacpp-docker-section').hide();
+            $('#install-llamacpp-pip-section').show();
+        }
+    }
+
+    $.update_table();
+    $.gen_content();
+});
diff --git a/index.rst b/index.rst
@@ -32,6 +32,7 @@
    sources/lm_evaluation/index.rst
    sources/wenet/index.rst
    sources/whisper_cpp/index.rst
+   sources/llama_cpp/index.rst
 
 .. warning::
 
@@ -263,5 +264,23 @@
                     <a href="sources/whisper_cpp/quick_start.html">快速上手</a>
                 </div>
             </div>
+            <!-- Card 13 -->
+            <div class="box rounded-lg p-4 flex flex-col items-center">
+                <div class="flex items-center mb-4">
+                    <div class="img w-16 h-16 rounded-md mr-4" style="background-image: url('_static/images/llama_cpp.png')"></div>
+                    <div>
+                        <h2 class="text-lg font-semibold">llama.cpp</h2>
+                        <p class="text-gray-600 desc">由C/C++实现的 Meta LLaMa 架构</p>
+                    </div>
+                </div>
+                <div class="flex-grow"></div>
+                <div class="flex space-x-4 text-blue-600">
+                    <a href="https://github.com/ggerganov/llama.cpp">官方链接</a>
+                    <span class="split">|</span>
+                    <a href="sources/llama_cpp/install.html">安装指南</a>
+                    <span class="split">|</span>
+                    <a href="sources/llama_cpp/quick_start.html">快速上手</a>
+                </div>
+            </div>
         </div>
     </div>
diff --git a/sources/llama_cpp/index.rst b/sources/llama_cpp/index.rst
@@ -0,0 +1,10 @@
+llama.cpp
+===========
+
+.. toctree::
+   :maxdepth: 2
+
+   install.rst
+   quick_start.rst
+
+
diff --git a/sources/llama_cpp/install.rst b/sources/llama_cpp/install.rst
@@ -0,0 +1,125 @@
+安装指南
+==============
+
+本教程面向使用 llama.cpp & 昇腾的开发者，帮助完成昇腾环境下 llama.cpp 的安装。
+
+.. note::
+  目前 llama.cpp 仅支持 Atlas 300T A2 型号设备
+
+llama.cpp 下载安装
+---------------------------
+
+此处提供 docker 和 pip 两种安装方式，请按需选择:
+
+.. raw:: html
+
+    <script type="text/javascript" src="../../_static/llamacpp_actions.js"></script>
+    <div id="div-installation" style="">
+        <div class="row">
+            <div class="row-element-1" id="col-values">
+                    <div class="row" id="row-install_type">
+                    <div class="mobile-headings">安装方式</div>
+                    <div class="values-element block-3 install-type selected" id="install_type-pip">pip</div>
+                    <div class="values-element block-3 install-type" id="install_type-docker">Docker</div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+---------------
+
+.. raw:: html
+
+    <section id="install-llamacpp-pip-section">
+        <h2>使用源代码安装</h2>
+        <div class="admonition note">
+                <p class="admonition-title">备注</p>
+                <p>请确保已经根据快速安装昇腾环境指引<a class="reference internal" href="../ascend/quick_install.html"><span class="doc">安装</span></a>了对应的CANN-toolkit版本以及相应的固件和驱动，并应用了CANN-toolkit环境变量。</p>
+        </div>
+        <div class="admonition warning">
+            <p class="admonition-title">提示</p>
+            <p>LLAMA-Factory 支持的 CANN 最低版本为 8.0.rc1。安装 CANN 时，请同时安装 Kernel 算子包。</p>
+        </div>
+        <h3>Python 环境创建</h3>
+            <div class="code">
+                <p>创建并激活 Python 环境：</p>
+                <div class="highlight">
+                  <pre>conda create -y -n llamacpp python=3.10
+    conda activate llamacpp</pre>
+                </div>
+            </div>
+
+        <h3>获取源代码</h3>
+            <div class="code">
+                <p>使用以下 git 指令获取源码</p>
+                <div class="highlight">
+                <pre>git clone https://github.com/ggerganov/llama.cpp
+    cd llama.cpp</pre>
+                </div>
+            </div>
+
+        <h3>构建 llama.cpp </h3>
+            <div class="code">
+                <div class="highlight">
+                <pre>cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release
+    cmake --build build --config release</pre>
+                </div>
+            </div>
+
+    </section>
+
+    <div id="install-llamacpp-docker-section">
+        <section>
+            <h2>使用 Docker</h2>
+            <div class="admonition note">
+                <p class="admonition-title">备注</p>
+                <p>请确保已经根据快速安装昇腾环境指引<a class="reference internal" href="../ascend/quick_install.html"><span class="doc">安装</span></a>了对应的的固件和驱动。</p>
+            </div>
+            <div class="admonition tip">
+              <p class="admonition-title">提示</p>
+              <p>更多 CANN 的基础镜像选择见<a class="reference" href="https://hub.docker.com/r/ascendai/cann/tags"><span class="doc">ascendai/cann</span></a>。</p>
+            </div>           
+                <p>构建 docker 镜像：</p>
+                <div class="highlight">
+                <pre>docker build -t llama-cpp-cann -f .devops/llama-cli-cann.Dockerfile .</pre>
+                </div>
+                <p>找到所有卡的运行信息：</p>
+                <div class="highlight">
+                <pre>npu-smi info</pre>
+                </div>
+                <p>启动 docker 容器：</p>
+                <div class="highlight">
+                <pre>docker run --name llamacpp \
+    --device /dev/davinci0  \
+    --device /dev/davinci_manager \
+    --device /dev/devmm_svm \
+    --device /dev/hisi_hdc \
+    -v /usr/local/dcmi:/usr/local/dcmi \
+    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+    -v /PATH_TO_YOUR_MODELS/:/app/models \
+    -it llama-cpp-cann -m /app/models/MODEL_PATH -ngl 32 \
+    -p "Building a website can be done in 10 simple steps:"</pre>
+            </div>
+        </section>
+    </div>
+
+安装校验
+-----------------
+
+安装完成后，无任何报错信息，即为安装成功，下面为部分回显信息:
+
+.. code-block:: shell 
+    :linenos:
+
+    [ 97%] Built target test-grammar-integration
+    [ 97%] Built target llama-speculative
+    [ 97%] Built target llama-perplexity
+    [ 98%] Linking CXX executable ../../bin/llama-bench
+    [ 98%] Linking CXX executable ../bin/test-json-schema-to-grammar
+    [ 98%] Built target llama-bench
+    [ 98%] Built target test-json-schema-to-grammar
+    [100%] Linking CXX executable ../../bin/llama-server
+    [100%] Built target llama-server
+
diff --git a/sources/llama_cpp/quick_start.rst b/sources/llama_cpp/quick_start.rst
@@ -0,0 +1,179 @@
+快速开始
+============
+
+.. note::
+    阅读本篇前，请确保已按照 :doc:`安装指南 <./install>` 准备好昇腾环境及 llama.cpp ！
+
+本教程聚焦大语言模型（Large Language Model，LLM）的推理过程，以 Qwen2.5-7B 模型为例，讲述如何使用 llama.cpp 在昇腾 NPU 上进行推理。
+
+
+模型文件准备及量化
+---------------
+
+llama.cpp 的推理需要使用 gguf 格式文件，llama.cpp 提供了两种方式转换 Hugging Face 模型文件：
+
+- 使用 `GGUF-my-repo <https://huggingface.co/spaces/ggml-org/gguf-my-repo>`_ 将模型进行转换。
+
+- 使用项目中的 `convert_hf_to_gguf.py` 文件将 Hugging Face 模型转换为 gguf 格式:
+
+    .. code-block:: shell 
+        :linenos:
+
+        python convert_hf_to_gguf.py path/to/model
+
+详情请参考 `Prepare and Quantize <https://github.com/ggerganov/llama.cpp/blob/master/README.md#prepare-and-quantize>`_ 。
+
+注意：目前仅支持 FP16 精度及 Q4_0/Q8_0 量化模型。
+
+推理
+------------
+
+有两种设备选择模式:
+
+- 单设备：使用用户指定的一个设备目标。
+- 多设备：自动选择具有相同后端的设备。
+
++---------------+---------------------------------------------+
+|   设备选择    |    参数                                     |
++===============+=============================================+
+|   单设备      |    --split-mode none --main-gpu DEVICE_ID   |
++---------------+---------------------------------------------+
+|   多设备      |    --split-mode layer (default)             |
++---------------+---------------------------------------------+
+
+使用单卡推理
+++++++++++++++++
+
+.. code-block:: shell 
+    :linenos:
+
+    ./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
+
+使用多卡推理
+++++++++++++++++
+
+.. code-block:: shell 
+    :linenos:
+
+    ./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
+
+以下为正常推理结果：
+
+.. code-block:: shell
+    :linenos:
+
+    Log start
+    main: build = 3520 (8e707118)
+    main: built with cc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 for aarch64-linux-gnu
+    main: seed  = 1728907816
+    llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /home/jiahao/models/llama3-8b-instruct-fp16.gguf (version GGUF V3 (latest))
+    llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+    llama_model_loader: - kv   0:                       general.architecture str              = llama
+    llama_model_loader: - kv   1:                               general.name str              = Meta-Llama-3-8B-Instruct
+    llama_model_loader: - kv   2:                          llama.block_count u32              = 32
+    llama_model_loader: - kv   3:                       llama.context_length u32              = 8192
+    llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096
+    llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336
+    llama_model_loader: - kv   6:                 llama.attention.head_count u32              = 32
+    llama_model_loader: - kv   7:              llama.attention.head_count_kv u32              = 8
+    llama_model_loader: - kv   8:                       llama.rope.freq_base f32              = 500000.000000
+    llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
+    llama_model_loader: - kv  10:                          general.file_type u32              = 1
+    llama_model_loader: - kv  11:                           llama.vocab_size u32              = 128256
+    llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128
+    llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
+    llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = llama-bpe
+    llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+    llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+    llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
+    llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000
+    llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009
+    llama_model_loader: - kv  20:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...
+    llama_model_loader: - kv  21:               general.quantization_version u32              = 2
+    llama_model_loader: - type  f32:   65 tensors
+    llama_model_loader: - type  f16:  226 tensors
+    llm_load_vocab: special tokens cache size = 256
+    llm_load_vocab: token to piece cache size = 0.8000 MB
+    llm_load_print_meta: format           = GGUF V3 (latest)
+    llm_load_print_meta: arch             = llama
+    llm_load_print_meta: vocab type       = BPE
+    llm_load_print_meta: n_vocab          = 128256
+    llm_load_print_meta: n_merges         = 280147
+    llm_load_print_meta: vocab_only       = 0
+    llm_load_print_meta: n_ctx_train      = 8192
+    llm_load_print_meta: n_embd           = 4096
+    llm_load_print_meta: n_layer          = 32
+    llm_load_print_meta: n_head           = 32
+    llm_load_print_meta: n_head_kv        = 8
+    llm_load_print_meta: n_rot            = 128
+    llm_load_print_meta: n_swa            = 0
+    llm_load_print_meta: n_embd_head_k    = 128
+    llm_load_print_meta: n_embd_head_v    = 128
+    llm_load_print_meta: n_gqa            = 4
+    llm_load_print_meta: n_embd_k_gqa     = 1024
+    llm_load_print_meta: n_embd_v_gqa     = 1024
+    llm_load_print_meta: f_norm_eps       = 0.0e+00
+    llm_load_print_meta: f_norm_rms_eps   = 1.0e-05
+    llm_load_print_meta: f_clamp_kqv      = 0.0e+00
+    llm_load_print_meta: f_max_alibi_bias = 0.0e+00
+    llm_load_print_meta: f_logit_scale    = 0.0e+00
+    llm_load_print_meta: n_ff             = 14336
+    llm_load_print_meta: n_expert         = 0
+    llm_load_print_meta: n_expert_used    = 0
+    llm_load_print_meta: causal attn      = 1
+    llm_load_print_meta: pooling type     = 0
+    llm_load_print_meta: rope type        = 0
+    llm_load_print_meta: rope scaling     = linear
+    llm_load_print_meta: freq_base_train  = 500000.0
+    llm_load_print_meta: freq_scale_train = 1
+    llm_load_print_meta: n_ctx_orig_yarn  = 8192
+    llm_load_print_meta: rope_finetuned   = unknown
+    llm_load_print_meta: ssm_d_conv       = 0
+    llm_load_print_meta: ssm_d_inner      = 0
+    llm_load_print_meta: ssm_d_state      = 0
+    llm_load_print_meta: ssm_dt_rank      = 0
+    llm_load_print_meta: model type       = 8B
+    llm_load_print_meta: model ftype      = F16
+    llm_load_print_meta: model params     = 8.03 B
+    llm_load_print_meta: model size       = 14.96 GiB (16.00 BPW) 
+    llm_load_print_meta: general.name     = Meta-Llama-3-8B-Instruct
+    llm_load_print_meta: BOS token        = 128000 '<|begin_of_text|>'
+    llm_load_print_meta: EOS token        = 128009 '<|eot_id|>'
+    llm_load_print_meta: LF token         = 128 'Ä'
+    llm_load_print_meta: EOT token        = 128009 '<|eot_id|>'
+    llm_load_print_meta: max token length = 256
+    llm_load_tensors: ggml ctx size =    0.27 MiB
+    llm_load_tensors:        CPU buffer size = 15317.02 MiB
+    llm_load_tensors:       CANN buffer size = 13313.00 MiB
+    .........................................................................................
+    llama_new_context_with_model: n_ctx      = 8192
+    llama_new_context_with_model: n_batch    = 2048
+    llama_new_context_with_model: n_ubatch   = 512
+    llama_new_context_with_model: flash_attn = 0
+    llama_new_context_with_model: freq_base  = 500000.0
+    llama_new_context_with_model: freq_scale = 1
+    llama_kv_cache_init:       CANN KV buffer size =  1024.00 MiB
+    llama_new_context_with_model: KV self size  = 1024.00 MiB, K (f16):  512.00 MiB, V (f16):  512.00 MiB
+    llama_new_context_with_model:        CPU  output buffer size =     0.49 MiB
+    llama_new_context_with_model:       CANN compute buffer size =  1260.50 MiB
+    llama_new_context_with_model:        CPU compute buffer size =    24.01 MiB
+    llama_new_context_with_model: graph nodes  = 1030
+    llama_new_context_with_model: graph splits = 4
+
+    system_info: n_threads = 192 / 192 | AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | 
+    sampling: 
+        repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
+        top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
+        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
+    sampling order: 
+    CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature 
+    generate: n_ctx = 8192, n_batch = 2048, n_predict = -1, n_keep = 1
+
+
+    Building a website can be done in 10 simple steps: 1. Define your website's purpose and target audience 2. Choose a domain name and register it with a registrar 3. Select a web hosting service and set up your hosting account 4. Design your website's layout and structure 5. Create content for your website, including text, images, and other media 6. Build a responsive website design that adapts to different devices and screen sizes 7. Choose a Content Management System (CMS) and install it on your website 8. Customize your website's design and layout using a CMS
+
+    llama_print_timings:        load time =    9074.69 ms
+    llama_print_timings:      sample time =      31.97 ms /   112 runs   (    0.29 ms per token,  3503.28 tokens per second)
+    llama_print_timings: prompt eval time =     238.53 ms /    13 tokens (   18.35 ms per token,    54.50 tokens per second)
+    llama_print_timings:        eval time =   13152.29 ms /   111 runs   (  118.49 ms per token,     8.44 tokens per second)
+    llama_print_timings:       total time =   13623.53 ms /   124 tokens