From b073029f1edd3a278aa3e5a3da62642d9efc84aa Mon Sep 17 00:00:00 2001 From: zhengchenhui Date: Mon, 24 Nov 2025 20:18:22 +0800 Subject: [PATCH] =?UTF-8?q?=E6=8F=90=E4=BA=A4ANNC=E7=94=A8=E6=88=B7?= =?UTF-8?q?=E6=8C=87=E5=8D=97=E8=87=B32403-LTS-SP3=E5=88=86=E6=94=AF.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/zh/annc/_toc.yaml | 6 + docs/zh/annc/annc_user_manual.md | 314 +++++++++++++++++++++++++++++++ 2 files changed, 320 insertions(+) create mode 100644 docs/zh/annc/_toc.yaml create mode 100644 docs/zh/annc/annc_user_manual.md diff --git a/docs/zh/annc/_toc.yaml b/docs/zh/annc/_toc.yaml new file mode 100644 index 0000000..000b51b --- /dev/null +++ b/docs/zh/annc/_toc.yaml @@ -0,0 +1,6 @@ +label: ANNC使用手册 +isManual: true +description: ANNC是专注于加速神经网络计算的编译器，加速推荐和大模型的推理性能，支持主流开源推理框架接入。 +sections: + - label: ANNC使用手册 + href: ./annc_user_manual.md \ No newline at end of file diff --git a/docs/zh/annc/annc_user_manual.md b/docs/zh/annc/annc_user_manual.md new file mode 100644 index 0000000..5d1a305 --- /dev/null +++ b/docs/zh/annc/annc_user_manual.md @@ -0,0 +1,314 @@ +# ANNC 使用手册 + +## 1 ANNC 介绍 + +ANNC（Accelerated Neural Network Compiler）是专注于加速神经网络计算的编译器，旨在通过计算图优化、高性能融合算子生成与对接技术、以及高效的代码生成与优化能力，提升推荐和大模型的推理性能，支持主流开源推理框架接入。 + +## 2 ANNC 的安装构建 + +### 2.1 直接安装ANNC（通过eur获取） + +```bash +wget https://eur.openeuler.openatom.cn/results/lesleyzheng1103/ANNC/openeuler-22.03_LTS_SP4-aarch64/00110327-ANNC/ANNC-0.0.2-3.aarch64.rpm + +# 安装到 / 目录 +rpm -ivh ANNC-0.0.2-3.aarch64.rpm +``` + +### 2.2 RPM包构建安装流程（推荐） + +1. 使用 root 权限，安装 rpmbuild、rpmdevtools，具体命令如下： + + ```bash + # 安装 rpmbuild + yum install dnf-plugins-core rpm-build + # 安装 rpmdevtools + yum install rpmdevtools + ``` + +2. 在主目录`/root`下生成 rpmbuild 文件夹： + + ```bash + rpmdev-setuptree + # 检查自动生成的目录结构 + ls ~/rpmbuild/ + BUILD BUILDROOT RPMS SOURCES SPECS SRPMS + ``` + +3. 使用`git clone -b master https://gitee.com/src-openeuler/ANNC.git`，从目标仓库的 `master` 分支拉取代码，并把目标文件放入 rpmbuild 的相应文件夹下： + + ``` shell + cp ANNC/*.tar.gz* ~/rpmbuild/SOURCES + cp ANNC/*.patch ~/rpmbuild/SOURCES/ + cp ANNC/ANNC.spec ~/rpmbuild/SPECS/ + ``` + +4. 用户可通过以下步骤生成 `ANNC` 的 RPM 包： + + ```bash + # 安装 ANNC 所需依赖 + yum-builddep ~/rpmbuild/SPECS/ANNC.spec + # 构建 ANNC 依赖包 + # 若出现 check-rpaths 相关报错，则需要在 rpmbuild 前添加 QA_RPATHS=0x0002，例如 + # QA_RPATHS=0x0002 rpmbuild -ba ~/rpmbuild/SPECS/ANNC.spec + rpmbuild -ba ~/rpmbuild/SPECS/ANNC.spec + # 安装 RPM 包 + cd ~/rpmbuild/RPMS/ + rpm -ivh ANNC--..rpm + ``` + + 注意事项：若系统因存有旧版本的 RPM 安装包而导致文件冲突，可以通过以下方式解决： + + ```bash + # 解决方案一：强制安装新版本 + rpm -ivh ANNC--..rpm --force + # 解决方案二：更新安装包 + rpm -Uvh ANNC--..rpm + ``` + +### 2.3 源码构建安装流程 + +ANNC 的源码地址：。 + +保证以下依赖包已安装： + +```shell +yum install -y gcc gcc-c++ bzip2 python3-devel python3-numpy python3-setuptools python3-wheel libstdc++-static java-11-openjdk java-11-openjdk-devel make +``` + +安装bazel，从该地址 获取bazel-6.5.0包。 + +```bash +unzip bazel-6.5.0-dist.zip -d bazel-6.5.0 +cd bazel-6.5.0 +env EXTRA_BAZEL_ARGS="--tool_java_runtime_version=local_jdk" bash ./compile.sh + +export PATH=/path/to/bazel-6.5.0/output:$PATH +bazel --version +``` + +准备XNNPACK。 + +```bash +git clone https://gitee.com/openeuler/ANNC.git +export ANNC="your_path_to_ANNC" + +cd $ANNC/annc/service/cpu/xla/libs +bash xnnpack.sh + +cd $ANNC/annc/service/cpu/xla/libs/XNNPACK/build +cp libXNNPACK.so /usr/lib64 +export XNNPACK_BASE="$ANNC/annc/service/cpu/xla/libs" +export XNNPACK_DIR="$XNNPACK_BASE/XNNPACK" + +CPLUS_INCLUDE_PATH+="$ANNC/annc/service/cpu/xla/:" +CPLUS_INCLUDE_PATH+="$ANNC/annc/service/:" +CPLUS_INCLUDE_PATH+="$XNNPACK_DIR/:" +CPLUS_INCLUDE_PATH+="$XNNPACK_DIR/include/:" +CPLUS_INCLUDE_PATH+="$XNNPACK_DIR/src/:" +CPLUS_INCLUDE_PATH+="$XNNPACK_DIR/build/pthreadpool-source/include/:" +export CPLUS_INCLUDE_PATH +``` + +安装ANNC，从源码地址下载ANNC源码包。 + +```bash +cd $ANNC + +bash build.sh + +cp bazel-bin/annc/service/cpu/libannc.so /usr/lib64 +mkdir -p /usr/include/annc +cp annc/service/cpu/kdnn_rewriter.h /usr/include/annc +cd python +python3 setup.py bdist_wheel +python3 -m pip install dist/*.whl +``` + +## 3 使用流程 + +>[!NOTE]注意 +>ANNC使用者需提前部署好tf-serving，通过编译选项和代码补丁的方式接入ANNC编译优化扩展套件。 + +### 3.1 图融合结合手动大算子 + +下载基线模型。 + +```bash +git clone https://gitee.com/openeuler/sra_benchmark.git +``` + +从基线模型库中获取以下目标推荐模型 **DeepFM、DFFM、DLRM、W&D**。 + +通过以下命令行实现图融合。 + +```bash +# 安装依赖库 + +python3 -m pip install tensorflow==2.15.1 + +# 运行模型转换，以DeepFM模型为例 + +annc-opt -I /path/to/model_DeepFM/1730800001/1 -O deepfm_new/1 dnn_sparse linear_sparse +cp -r /path/to/model_DeepFM/1730800001/1/variables deepfm_new/1 +``` + +完成上述命令后，输出目录`deepfm_new/1`下应生成新的模型文件`saved_model.pbtxt`，搜索`KPFusedSparseEmbedding`，确认图融合算子正确生成。 + +然后将ANNC提供的开源算子库注册到tf-serving： + +```bash +# 进入tf-serving目录，创建自定义算子文件夹 + +cd /path/to/serving +mkdir tensorflow_serving/custom_ops + +# 将ANNC算子拷贝到该目录下 + +cp /usr/include/annc/fused*.cc tensorflow_serving/custom_ops/ +``` + +创建算子编译文件`tensorflow_serving/custom_ops/BUILD`，并在该文件中写入以下内容： + +```ini +package( + default_visibility = [ + "//visibility:public", + ], + licenses = ["notice"], +) + +cc_library( + name = 'recom_embedding_ops', + srcs = [ + "fused_sparse_embedding.cc", + "fused_linear_embedding_with_hash_bucket.cc", + "fused_dnn_embedding_with_hash_bucket.cc" + ], + alwayslink = 1, + deps = [ + "@org_tensorflow//tensorflow/core:framework", + ] +) +``` + +```bash +# 打开 tensorflow_serving/model_servers/BUILD，搜索SUPPORTED_TENSORFLOW_OPS，添加以下内容注册我们的算子: + +"//tensorflow_serving/custom_ops:recom_embedding_ops" +``` + +完成算子注册后，使用以下命令重新编译tf-serving，编译成功即表示算子成功注册： + +```bash +bazel --output_user_root=./output build -c opt --distdir=./proxy \ + --define tflite_with_xnnpack=false \ + tensorflow_serving/model_servers:tensorflow_model_server +``` + +### 3.2 使能算子优化和图优化 + +在构建好的server的tensorflow的xla路径下，通过补丁脚本使能以下补丁： + +```bash +export TF_PATH="$HOME/serving/output/XXX/external/org_tensorflow" +export XLA_PATH="$HOME/serving/output/XXX/external/org_tensorflow/third_party/xla" + +# 通过方式一安装的ANNC： +cd /usr/include/annc/tfserver/xla + +# 修改xla2.sh前两行为： +TF_PATCH_PATH="$ANNC" +PATH_OF_PATCHES="$ANNC/xla" +export ANNC_PATH=/usr/include/annc +bash xla2.sh + +# 通过方式二安装的ANNC： +cd $ANNC/install/tfserver/xla +export ANNC_PATH=$ANNC +bash xla2.sh + +# 重新编译 +bazel --output_user_root=./output build -c opt --distdir=./proxy \ + --define tflite_with_xnnpack=false \ + tensorflow_serving/model_servers:tensorflow_model_server +``` + +### 3.3 图优化 + +设置环境变量，开启优化特性。 + +```bash +export 'TF_XLA_FLAGS=--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit --tf_xla_min_cluster_size=16' +export OMP_NUM_THREADS=1 +export PORT=7004 # 端口号 +ANNC_FLAGS="--graph-opt" ENABLE_BISHENG_GRAPH_OPT="" ./bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server +--port=$PORT --rest_api_port=7005 +--model_base_path=/path/to/model_Boss/ +--model_name=deepfm +--tensorflow_intra_op_parallelism=1 --tensorflow_inter_op_parallelism=-1 +--xla_cpu_compilation_enabled=true +``` + +### 3.4 算子优化 + +配置环境变量`ANNC_FLAGS`，开启MatMul下发和对接OpenBLAS优化选项，启动TF-Serving，指定目标模型。 + +```bash +export 'TF_XLA_FLAGS=--tf_xla_auto_jit=2 --tf_xla_cpu_global_jit --tf_xla_min_cluster_size=16' +export OMP_NUM_THREADS=1 +export PORT=7004 # 端口号 +ANNC_FLAGS="--gemm-opt" XLA_FLAGS="--xla_cpu_enable_xnnpack=true" ./bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server \ + --port=$PORT --rest_api_port=7005 \ + --model_base_path=/path/to/model_DeepFM/1730800001/ \ + --model_name=deepfm \ + --tensorflow_intra_op_parallelism=1 --tensorflow_inter_op_parallelism=-1 \ + --xla_cpu_compilation_enabled=true +``` + +### 3.5 Remapper(Tensorflow)图融合优化 + +该特性基于原生Tensorflow框架开发，在Remapper优化器中调用ANNC graph optimizer优化。 + +#### 步骤1：下载Tensorflow2.15版本 + +```bash +git clone https://gitee.com/mirrors/tensorflow.git -b v2.15.0 +``` + +#### 步骤2：应用ANNC补丁和融合算子 + +```bash +export ANNC="your_path_to_ANNC" + +cd tensorflow +patch -p1 < $ANNC/annc/tensorflow/tf_annc_optimizer.patch +cp -r $ANNC/annc/tensorflow/graph_optimizer ./tensorflow/core/grappler/optimizers/ +cp $ANNC/annc/tensorflow/kernels/* ./tensorflow/core/kernels/ +cp $ANNC/annc/tensorflow/ops/* ./tensorflow/core/ops/ +cp $ANNC/annc/tensorflow/api_def/* ./tensorflow/core/api_def/python_api/ +cp $ANNC/annc/tensorflow/api_def/* ./tensorflow/core/api_def/base_api/ +``` + +#### 步骤3：编译tensorflow + +```bash +bazel build --config=v2 --config=xla --config=noaws --distdir=./proxy //tensorflow:tensorflow_cc + +cd ./bazel-bin/tensorflow + +ln -s libtensorflow_framework.so.2.15.0 libtensorflow_framework.so.2 +ln -s libtensorflow_framework.so.2 libtensorflow_framework.so +ln -s libtensorflow_cc.so.2.15.0 libtensorflow_cc.so.2 +ln -s libtensorflow_cc.so.2 libtensorflow_cc.so + +# 配置环境变量 +export LD_LIBRARY_PATH=/path_to_tensorflow/bazel-bin/tensorflow +``` + +#### 步骤4：使能图融合优化 + +```bash +# 使用Tensorflwo推理时，开启优化选项即可： +ANNC_FUASED_ALL = 1 +``` -- Gitee