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-如果您已经学会使用飞桨来完成常规任务，期望了解更多飞桨在工业部署方面的能力，请阅读： - - - - `预测与部署 <../advanced_guide/inference_deployment/index_cn.html>`_ ：介绍如何应用训练好的模型进行预测 - -.. toctree:: - :hidden: - - inference_deployment/index_cn.rst - flags/flags_cn.rst diff --git a/docs/advanced_guide/index_en.rst b/docs/advanced_guide/index_en.rst deleted file mode 100644 index f44212dadfb..00000000000 --- a/docs/advanced_guide/index_en.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. _user_guide_en_: - -#################### -Advanced User Guides -#################### - -.. todo:: - -So far you have already been familiar with PaddlePaddle. And the next expectation, read more on: - - - - `Deploy Inference Model `_ ：How to deploy the trained network to perform practical inference - - -.. toctree:: - :hidden: - - inference_deployment/index_en.rst - flags/flags_en.rst diff --git a/docs/advanced_guide/performance_improving/amp/amp.md b/docs/advanced_guide/performance_improving/amp/amp.md deleted file mode 100644 index db053907dd2..00000000000 --- a/docs/advanced_guide/performance_improving/amp/amp.md +++ /dev/null @@ -1,171 +0,0 @@ -# 混合精度训练最佳实践 - -Automatic Mixed Precision (AMP) 是一种自动混合使用半精度（FP16）和单精度（FP32）来加速模型训练的技术。AMP 技术可方便用户快速将使用 FP32 训练的模型修改为使用混合精度训练，并通过黑白名单和动态`loss scaling`来保证训练时的数值稳定性进而避免梯度 Infinite 或者 NaN(Not a Number)。借力于新一代 NVIDIA GPU 中 Tensor Cores 的计算性能，PaddlePaddle AMP 技术在 ResNet50、Transformer 等模型上训练速度相对于 FP32 训练加速比可达 1.5～2.9。 - -### 半精度浮点类型 FP16 - -如图 1 所示，半精度（Float Precision16，FP16）是一种相对较新的浮点类型，在计算机中使用 2 字节（16 位）存储。在 IEEE 754-2008 标准中，它亦被称作 binary16。与计算中常用的单精度（FP32）和双精度（FP64）类型相比，FP16 更适于在精度要求不高的场景中使用。 - -
- missing -
图 1. 半精度和单精度数据示意图
-
- -### 英伟达 GPU 的 FP16 算力 - -在使用相同的超参数下，混合精度训练使用半精度浮点（FP16）和单精度（FP32）浮点即可达到与使用纯单精度训练相同的准确率，并可加速模型的训练速度。这主要得益于英伟达推出的 Volta 及 Turing 架构 GPU 在使用 FP16 计算时具有如下特点： - -* FP16 可降低一半的内存带宽和存储需求，这使得在相同的硬件条件下研究人员可使用更大更复杂的模型以及更大的 batch size 大小。 -* FP16 可以充分利用英伟达 Volta 及 Turing 架构 GPU 提供的 Tensor Cores 技术。在相同的 GPU 硬件上，Tensor Cores 的 FP16 计算吞吐量是 FP32 的 8 倍。 - -### PaddlePaddle AMP 功能——牛刀小试 - -如前文所述，使用 FP16 数据类型可能会造成计算精度上的损失，但对深度学习领域而言，并不是所有计算都要求很高的精度，一些局部的精度损失对最终训练效果影响很微弱，却能使吞吐和训练速度带来大幅提升。因此，混合精度计算的需求应运而生。具体而言，训练过程中将一些对精度损失不敏感且能利用 Tensor Cores 进行加速的运算使用半精度处理，而对精度损失敏感部分依然保持 FP32 计算精度，用以最大限度提升访存和计算效率。 - -为了避免对每个具体模型人工地去设计和尝试精度混合的方法，PaddlePaddle 框架提供自动混合精度训练（AMP）功能，解放"炼丹师"的双手。在 PaddlePaddle 中使用 AMP 训练是一件十分容易的事情，用户只需要增加一行代码即可将原有的 FP32 训练转变为 AMP 训练。下面以`MNIST`为例介绍 PaddlePaddle AMP 功能的使用示例。 - -**MNIST 网络定义** - -```python -import paddle.fluid as fluid - -def MNIST(data, class_dim): - conv1 = fluid.layers.conv2d(data, 16, 5, 1, act=None, data_format='NHWC') - bn1 = fluid.layers.batch_norm(conv1, act='relu', data_layout='NHWC') - pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2, data_format='NHWC') - conv2 = fluid.layers.conv2d(pool1, 64, 5, 1, act=None, data_format='NHWC') - bn2 = fluid.layers.batch_norm(conv2, act='relu', data_layout='NHWC') - pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2, data_format='NHWC') - fc1 = fluid.layers.fc(pool2, size=64, act='relu') - fc2 = fluid.layers.fc(fc1, size=class_dim, act='softmax') - return fc2 -``` - -针对 CV(Computer Vision)类模型组网，为获得更高的训练性能需要注意如下三点： - -* `conv2d`、`batch_norm`以及`pool2d`等需要将数据布局设置为`NHWC`，这样有助于使用 TensorCore 技术加速计算过程1。 -* Tensor Cores 要求在使用 FP16 加速卷积运算时 conv2d 的输入/输出通道数为 8 的倍数2，因此设计网络时推荐将 conv2d 层的输入/输出通道数设置为 8 的倍数。 -* Tensor Cores 要求在使用 FP16 加速矩阵乘运算时矩阵行数和列数均为 8 的倍数3，因此设计网络时推荐将 fc 层的 size 参数设置为 8 的倍数。 - - -**FP32 训练** - -为了训练 MNIST 网络，还需要定义损失函数来更新权重参数，此处使用的优化器是 SGDOptimizer。为了简化说明，这里省略了迭代训练的相关代码，仅体现损失函数及优化器定义相关的内容。 - -```python -import paddle -import numpy as np - -data = fluid.layers.data( - name='image', shape=[None, 28, 28, 1], dtype='float32') -label = fluid.layers.data(name='label', shape=[None, 1], dtype='int64') - -out = MNIST(data, class_dim=10) -loss = fluid.layers.cross_entropy(input=out, label=label) -avg_loss = fluid.layers.mean(loss) - -sgd = fluid.optimizer.SGDOptimizer(learning_rate=1e-3) -sgd.minimize(avg_loss) -``` - -**AMP 训练** - -与 FP32 训练相比，用户仅需使用 PaddlePaddle 提供的`fluid.contrib.mixed_precision.decorate` 函数将原来的优化器 SGDOptimizer 进行封装，然后使用封装后的优化器（mp_sgd）更新参数梯度即可完成向 AMP 训练的转换，代码如下所示： - -```python -sgd = SGDOptimizer(learning_rate=1e-3) -# 此处只需要使用 fluid.contrib.mixed_precision.decorate 将 sgd 封装成 AMP 训练所需的 -# 优化器 mp_sgd，并使用 mp_sgd.minimize(avg_loss)代替原来的 sgd.minimize(avg_loss)语句即可。 -mp_sgd = fluid.contrib.mixed_precision.decorator.decorate(sgd) -mp_sgd.minimize(avg_loss) -``` - -运行上述混合精度训练 python 脚本时为得到更好的执行性能可配置如下环境参数，并保证 cudnn 版本在 7.4.1 及以上。 - -```shell -export FLAGS_conv_workspace_size_limit=1024 # MB，根据所使用的 GPU 显存容量及模型特点设置数值，值越大越有可能选择到更快的卷积算法 -export FLAGS_cudnn_exhaustive_search=1 # 使用穷举搜索方法来选择快速卷积算法 -export FLAGS_cudnn_batchnorm_spatial_persistent=1 # 用于触发 batch_norm 和 relu 的融合 -``` - -上述即为最简单的 PaddlePaddle AMP 功能使用方法。ResNet50 模型的 AMP 训练示例可[点击此处](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/image_classification/README.md#%E6%B7%B7%E5%90%88%E7%B2%BE%E5%BA%A6%E8%AE%AD%E7%BB%83)查看，其他模型使用 PaddlePaddle AMP 的方法也与此类似。若 AMP 训练过程中出现连续的 loss nan 等不收敛现象，可尝试使用[check nan inf 工具](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/flags/check_nan_inf_cn.html#span-id-speed-span)进行调试。 - - -### PaddlePaddle AMP 功能——进阶使用 - -上一小节所述均为默认 AMP 训练行为，用户当然也可以改变一些默认的参数设置来满足特定的模型训练场景需求。接下来的章节将介绍 PaddlePaddle AMP 功能使用中用户可配置的参数行为，即进阶使用技巧。 - -#### 自定义黑白名单 - -PaddlePaddle AMP 功能实现中根据 FP16 数据类型计算稳定性和加速效果在框架内部定义了算子（Op）的黑白名单。具体来说，将对 FP16 计算友好且能利用 Tensor Cores 的 Op 归类于白名单，将使用 FP16 计算会导致数值不稳定的 Op 归类于黑名单，将对 FP16 计算没有多少影响的 Op 归类于灰名单。然而，框架开发人员不可能考虑到所有的网络模型情况，尤其是那些特殊场景中使用到的模型。用户可以在使用`fluid.contrib.mixed_precision.decorate` 函数时通过指定自定义的黑白名单列表来改变默认的 FP16 计算行为。 - -```python -sgd = SGDOptimizer(learning_rate=1e-3) -# list1 是白名单 op 列表，list2 是黑名单 op 列表，list3 是黑名单 var_name 列表（凡是以这些黑名单 var_name 为输入或输出的 op 均会被视为黑名单 op） -amp_list = AutoMixedPrecisionLists(custom_white_list=list1, custom_black_list=list2, custom_black_varnames=list3) -mp_sgd = fluid.contrib.mixed_precision.decorator.decorate(sgd, amp_list) -mp_sgd.minimize(avg_loss) -``` - -#### 自动 loss scaling - -为了避免梯度 Infinite 或者 NAN，PaddlePaddle AMP 功能支持根据训练过程中梯度的数值自动调整 loss scale 值。用户在使用`fluid.contrib.mixed_precision.decorate` 函数时也可以改变与 loss scaling 相关的参数设置，示例如下： - -```python -sgd = SGDOptimizer(learning_rate=1e-3) -mp_sgd = fluid.contrib.mixed_precision.decorator.decorate(sgd, - amp_lists=None, - init_loss_scaling=2**8, - incr_every_n_steps=500, - decr_every_n_nan_or_inf=4, - incr_ratio=2.0, - decr_ratio=0.5, - use_dynamic_loss_scaling=True) -mp_sgd.minimize(avg_loss) -``` - -`init_loss_scaling `、`incr_every_n_steps` 以及`decr_every_n_nan_or_inf`等参数控制着自动 loss scaling 的行为。它们仅当 `use_dynamic_loss_scaling`设置为 True 时有效。下面详述这些参数的意义： - -* init_loss_scaling(float)：初始 loss scaling 值。 -* incr_every_n_steps(int)：每经过 incr_every_n_steps 个连续的正常梯度值才会增大 loss scaling 值。 -* decr_every_n_nan_or_inf(int)：每经过 decr_every_n_nan_or_inf 个连续的无效梯度值(nan 或者 inf)才会减小 loss scaling 值。 -* incr_ratio(float)：每次增大 loss scaling 值的扩增倍数，其为大于 1 的浮点数。 -* decr_ratio(float)：每次减小 loss scaling 值的比例系数，其为小于 1 的浮点数。 - -### 多卡 GPU 训练的优化 - -PaddlePaddle AMP 功能对多卡 GPU 训练进行了深度优化。如图 2 所示，优化之前的参数梯度更新特点：梯度计算时虽然使用的是 FP16 数据类型，但是不同 GPU 卡之间的梯度传输数据类型仍为 FP32。 - -
- missing -
图 2. 不同 GPU 卡之间传输梯度使用 FP32 数据类型（优化前）
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- -为了降低 GPU 多卡之间的梯度传输带宽，我们将梯度传输提前至`Cast`操作之前，而每个 GPU 卡在得到对应的 FP16 梯度后再执行`Cast`操作将其转变为 FP32 类型，具体操作详见图 2。这一优化在训练大模型时对减少带宽占用尤其有效，如多卡训练 BERT-Large 模型。 - -
- missing -
图 3. 不同 GPU 卡之间传输梯度使用 FP16 数据类型（优化后）
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- -### 训练性能对比（AMP VS FP32） - -PaddlePaddle AMP 技术在 ResNet50、Transformer 等模型上训练速度相对于 FP32 训练上均有可观的加速比，下面是 ResNet50 和 ERNIE Large 模型的 AMP 训练相对于 FP32 训练的加速效果。 - - - - - - - -
图 4. Paddle AMP 训练加速效果（横坐标为卡数，如 8*8 代表 8 机 8 卡）
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- -从图 4 所示的图表可以看出，ResNet50 的 AMP 训练相对与 FP32 训练加速比可达$2.8 \times$以上，而 ERNIE Large 的 AMP 训练相对与 FP32 训练加速比亦可达 $1.7 \times -- 2.1 \times$ 。 - -### 参考文献 - -*

Mixed Precision Training

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使用自动混合精度加速 PaddlePaddle 训练

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Tensor Layouts In Memory: NCHW vs NHWC ↩

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Channels In And Out Requirements ↩

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Matrix-Matrix Multiplication Requirements ↩

diff --git a/docs/advanced_guide/performance_improving/index_cn.rst b/docs/advanced_guide/performance_improving/index_cn.rst deleted file mode 100644 index b50f091f8c7..00000000000 --- a/docs/advanced_guide/performance_improving/index_cn.rst +++ /dev/null @@ -1,16 +0,0 @@ -######## -性能调优 -######## - -.. toctree:: - :maxdepth: 1 - - singlenode_training_improving/training_best_practice.rst - singlenode_training_improving/memory_optimize.rst - device_switching/device_switching.md - amp/amp.md - multinode_training_improving/cpu_train_best_practice.rst - multinode_training_improving/dist_training_gpu.rst - multinode_training_improving/gpu_training_with_recompute.rst - inference_improving/paddle_tensorrt_infer.md - analysis_tools/index_cn.rst diff --git a/docs/advanced_guide/performance_improving/index_en.rst b/docs/advanced_guide/performance_improving/index_en.rst deleted file mode 100644 index f57e2a3d060..00000000000 --- a/docs/advanced_guide/performance_improving/index_en.rst +++ /dev/null @@ -1,12 +0,0 @@ -############### -Practice Improving -############### - -.. toctree:: - :maxdepth: 1 - - singlenode_training_improving/memory_optimize_en.rst - multinode_training_improving/cpu_train_best_practice_en.rst - multinode_training_improving/gpu_training_with_recompute_en.rst - inference_improving/paddle_tensorrt_infer_en.md - analysis_tools/index_en.rst diff --git a/docs/advanced_guide/performance_improving/inference_improving/paddle_xpu_infer_cn.md b/docs/advanced_guide/performance_improving/inference_improving/paddle_xpu_infer_cn.md deleted file mode 100644 index 8818e259155..00000000000 --- a/docs/advanced_guide/performance_improving/inference_improving/paddle_xpu_infer_cn.md +++ /dev/null @@ -1,120 +0,0 @@ -# 使用昆仑预测 - -百度的昆仑芯⽚是一款⾼性能的 AI SoC 芯⽚，⽀持推理和训练。昆仑芯⽚采⽤百度的先进 AI 架构，⾮常适合常⽤的深度学习和机器学习算法的云端计算需求，并能适配诸如⾃然语⾔处理、⼤规模语⾳识别、⾃动驾驶、⼤规模推荐等多种终端场景的计算需求。 - -Paddle Inference 集成了[Paddle-Lite 预测引擎](https://www.paddlepaddle.org.cn/lite/develop/demo_guides/kunlunxin_xpu.html)在昆仑 xpu 上进行预测部署。 - -## 编译注意事项 - -请确保编译的时候设置了 WITH_LITE=ON，且 XPU_SDK_ROOT 设置了正确的路径。 - -## 使用介绍 - -在使用 Predictor 时，我们通过配置 Config 中的接口，在 XPU 上运行。 - -```c++ -config->EnableLiteEngine( - precision_mode=PrecisionType::kFloat32, - zero_copy=false, - passes_filter={}, - ops_filter={}, -) -``` - -- **`precision_mode`**，类型：`enum class PrecisionType {kFloat32 = 0, kHalf, kInt8,};`, 默认值为`PrecisionType::kFloat32`。指定 lite 子图的运行精度。 -- **`zero_copy`**，类型：bool，lite 子图与 Paddle 之间的数据传递是否是零拷贝模式。 -- **`passes_filter`**，类型：`std::vector`，默认为空，扩展借口，暂不使用。 -- **`ops_filer`**，类型：`std::vector`，默认为空，显示指定哪些 op 不使用 lite 子图运行。 - -Python 接口如下： - -```python -config.enable_lite_engine( - precision_mode=PrecisionType.Float32, - zero_copy=False, - passes_filter=[], - ops_filter=[] -) -``` - -### Python demo - -因目前 Paddle-Inference 目前未将 xpu sdk 打包到 whl 包内，所以需要用户下载 xpu sdk，并加入到环境变量中，之后会考虑解决该问题。 - -下载[xpu_tool_chain](https://paddle-inference-dist.bj.bcebos.com/inference_demo/xpu_tool_chain.tgz)，解压后将 shlib 加入到 LD_LIBRARY_PATH - -``` -tar xzf xpu_tool_chain.tgz -``` -``` -export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$PWD/output/XTDK/shlib/:$PWD/output/XTDK/runtime/shlib/ -``` - -下载[resnet50](https://paddle-inference-dist.bj.bcebos.com/inference_demo/python/resnet50/ResNet50.tar.gz)模型，并解压，运行如下命令将会调用预测引擎 - -```bash -python resnet50_subgraph.py --model_file ./ResNet50/model --params_file ./ResNet50/params -``` - -resnet50_subgraph.py 的内容是： - -``` -import argparse -import time -import numpy as np -from paddle.inference import Config, PrecisionType -from paddle.inference import create_predictor - -def main(): - args = parse_args() - - config = set_config(args) - - predictor = create_predictor(config) - - input_names = predictor.get_input_names() - input_handle = predictor.get_input_handle(input_names[0]) - - fake_input = np.ones((args.batch_size, 3, 224, 224)).astype("float32") - input_handle.reshape([args.batch_size, 3, 224, 224]) - input_handle.copy_from_cpu(fake_input) - - for i in range(args.warmup): - predictor.run() - - start_time = time.time() - for i in range(args.repeats): - predictor.run() - - output_names = predictor.get_output_names() - output_handle = predictor.get_output_handle(output_names[0]) - output_data = output_handle.copy_to_cpu() - end_time = time.time() - print(output_data[0, :10]) - print('time is: {}'.format((end_time-start_time)/args.repeats * 1000)) - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", type=str, help="model dir") - parser.add_argument("--model_file", type=str, help="model filename") - parser.add_argument("--params_file", type=str, help="parameter filename") - parser.add_argument("--batch_size", type=int, default=1, help="batch size") - parser.add_argument("--warmup", type=int, default=0, help="warmup") - parser.add_argument("--repeats", type=int, default=1, help="repeats") - parser.add_argument("--math_thread_num", type=int, default=1, help="math_thread_num") - - return parser.parse_args() - -def set_config(args): - config = Config(args.model_file, args.params_file) - config.enable_lite_engine(PrecisionType.Float32, True) - # use lite xpu subgraph - config.enable_xpu(10 * 1024 * 1024) - # use lite cuda subgraph - # config.enable_use_gpu(100, 0) - config.set_cpu_math_library_num_threads(args.math_thread_num) - return config - -if __name__ == "__main__": - main() -``` diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice.rst deleted file mode 100644 index 6df05163486..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice.rst +++ /dev/null @@ -1,161 +0,0 @@ -.. _api_guide_cpu_training_best_practice: - -#################### -分布式 CPU 训练优秀实践 -#################### - -提高 CPU 分布式训练的训练速度，主要要从四个方面来考虑： -1）提高训练速度，主要是提高 CPU 的使用率；2）提高通信速度，主要是减少通信传输的数据量；3）提高数据 IO 速度；4）更换分布式训练策略，提高分布式训练速度。 - -提高 CPU 的使用率 -============= - -提高 CPU 使用率主要依赖 :code:`ParallelExecutor`，可以充分利用多个 CPU 的计算能力来加速计算。 - -简单实例用法： - -.. code-block:: python - - # 配置执行策略，主要是设置线程数 - exec_strategy = fluid.ExecutionStrategy() - exec_strategy.num_threads = 8 - - # 配置构图策略，对于 CPU 训练而言，应该使用 Reduce 模式进行训练 - build_strategy = fluid.BuildStrategy() - if int(os.getenv("CPU_NUM")) > 1: - build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce - - pe = fluid.ParallelExecutor( - use_cuda=False, - loss_name=avg_cost.name, - main_program=main_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy) - -以上参数中： - -- :code:`num_threads` ： 模型训练使用的线程数，最好和训练所在机器的物理 CPU 核数接近 -- :code:`reduce_strategy` ： 对于 CPU 训练而言，应该选择 fluid.BuildStrategy.ReduceStrategy.Reduce - - -通用环境变量配置： - -- :code:`CPU_NUM` ：模型副本 replica 的个数，最好和 num_threads 一致 - - -提高通信速度 -========== - -要减少通信数据量，提高通信速度，主要是使用稀疏更新 ，目前支持 :ref:`api_guide_sparse_update` 的主要是 :ref:`cn_api_fluid_layers_embedding` 。 - -.. code-block:: python - - data = fluid.layers.data(name='ids', shape=[1], dtype='int64') - fc = fluid.layers.embedding(input=data, size=[dict_size, 16], is_sparse=True) - -以上参数中： - -- :code:`is_sparse` ： 配置 embedding 使用稀疏更新，如果 embedding 的 dict_size 很大，而每次数据 data 很少，建议使用 sparse 更新方式。 - - -提高数据 IO 速度 -========== - -要提高 CPU 分布式的数据 IO 速度，可以首先考虑使用 dataset API 进行数据读取。 dataset 是一种多生产者多消费者模式的数据读取方法，默认情况下耦合数据读取线程与训练线程，在多线程的训练中，dataset 表现出极高的性能优势。 - -API 接口介绍可以参考： :ref:`cn_api_distributed_QueueDataset` - -结合实际的网络，比如 CTR-DNN 模型，引入的方法可以参考：https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/ctr/dnn - -最后使用 :code:`train_from_dataset` 接口来进行网络的训练： - -.. code-block:: python - - dataset = fluid.DatasetFactory().create_dataset() - exe = fluid.Executor(fluid.CPUPlace()) - exe.run(fluid.default_startup_program()) - exe.train_from_dataset(program=fluid.default_main_program(),dataset=dataset) - - -更换分布式训练策略 -========== - -CPU 分布式训练速度进一步提高的核心在于选择合适的分布式训练策略，比如定义通信策略、编译策略、执行策略等等。paddlepaddle 于 v1.7 版本发布了 :code:`DistributedStrategy` 功能，可以十分灵活且方便的指定分布式运行策略。 - -首先需要在代码中引入相关库： - -.. code-block:: python - - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet - import paddle.fluid.incubate.fleet.base.role_maker as role_maker - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy_factory import DistributedStrategyFactory - -然后指定 CPU 分布式运行的训练策略，目前可选配置有四种：同步训练（Sync）、异步训练（Async）、半异步训练（Half-Async）以及 GEO 训练。 - - -通过如下代码引入上述策略的默认配置，并进行 CPU 分布式训练： - -.. code-block:: python - - # step1: 引入 CPU 分布式训练策略 - # 同步训练策略 - strategy = DistributedStrategyFactory.create_sync_strategy() - # 半异步训练策略 - strategy = DistributedStrategyFactory.create_half_async_strategy() - # 异步训练策略 - strategy = DistributedStrategyFactory.create_async_strategy() - # GEO 训练策略 - strategy = DistributedStrategyFactory.create_geo_strategy(update_frequency=400) - - # step2: 定义节点角色 - role = role_maker.PaddleCloudRoleMaker() - fleet.init(role) - - # step3: 分布式训练 program 构建 - optimizer = fluid.optimizer.SGD(learning_rate) # 以 SGD 优化器为例 - optimizer = fleet.distributed_optimizer(optimizer, strategy) - optimizer.minimize(loss) - - # step4.1: 启动参数服务器节点（Server） - if fleet.is_server(): - fleet.init_server() - fleet.run_server() - - # step4.2: 启动训练节点（Trainer） - elif fleet.is_worker(): - fleet.init_worker() - exe.run(fleet.startup_program) - # Do training - exe.run(fleet.main_program) - fleet.stop_worker() - - -paddlepaddle 支持对训练策略中的细节进行调整： - -- 创建 compiled_program 所需的 build_strategy 及 exec_strategy 可以直接基于 strategy 获得 - -.. code-block:: python - - compiled_program = fluid.compiler.CompiledProgram(fleet.main_program).with_data_parallel( - loss_name=loss.name, - build_strategy=strategy.get_build_strategy(), - exec_strategy=strategy.get_execute_strategy()) - - -- 自定义训练策略细节，支持对 DistributeTranspilerConfig、TrainerRuntimeConfig、ServerRuntimeConfig、fluid.ExecutionStrategy、fluid.BuildStrategy 进行自定义配置。以 DistributeTranspilerConfig 为例，修改方式如下所示： - -.. code-block:: python - - strategy = DistributedStrategyFactory.create_sync_strategy() - - # 方式一（推荐）： - config = strategy.get_program_config() - config.min_block_size = 81920 - - - # 方式二：调用 set_program_config 修改组网相关配置，支持 DistributeTranspilerConfig 和 dict 两种数据类型 - config = DistributeTranspilerConfig() - config.min_block_size = 81920 - # config = dict() - # config['min_block_size'] = 81920 - strategy.set_program_config(config) diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice_en.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice_en.rst deleted file mode 100644 index 350606f34ea..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/cpu_train_best_practice_en.rst +++ /dev/null @@ -1,164 +0,0 @@ -.. _api_guide_cpu_training_best_practice_en: - -###################################################### -Best practices of distributed training on CPU -###################################################### - -To improve the training speed of CPU distributed training, we must consider two aspects: - -1. Improve the training speed mainly by improving utilization rate of CPU; -2. Improve the communication speed mainly by reducing the amount of data transmitted in the communication; -3. Improve the data IO speed by dataset API; -4. Improve the distributed training speed by changing distributed training strategy. - -Improve CPU utilization -============================= - -The CPU utilization mainly depends on :code:`ParallelExecutor`, which can make full use of the computing power of multiple CPUs to speed up the calculation. - -For detailed API usage, please refer to :ref:`api_fluid_ParallelExecutor` . A simple example: - -.. code-block:: python - - # Configure the execution strategy, mainly to set the number of threads - exec_strategy = fluid.ExecutionStrategy() - exec_strategy.num_threads = 8 - - # Configure the composition strategy, for CPU training, you should use the Reduce mode for training. - build_strategy = fluid.BuildStrategy() - if int(os.getenv("CPU_NUM")) > 1: - build_strategy.reduce_strategy=fluid.BuildStrategy.ReduceStrategy.Reduce - - pe = fluid.ParallelExecutor( - use_cuda=False, - loss_name=avg_cost.name, - main_program=main_program, - build_strategy=build_strategy, - exec_strategy=exec_strategy) - -Among the parameters above: - -- :code:`num_threads` : the number of threads used by the model training. It is preferably close to the number of the physical CPU cores of the machine where the training is performed. -- :code:`reduce_strategy` : For CPU training, you should choose fluid.BuildStrategy.ReduceStrategy.Reduce - - -Configuration of general environment variables: - -- :code:`CPU_NUM`: The number of replicas of the model, preferably the same as num_threads - - -Improve communication speed -============================== - -To reduce the amount of communication data and improve communication speed is achieved mainly by using sparse updates, the current support for `sparse update <../layers/sparse_update_en.html>`_ is mainly :ref:`api_fluid_layers_embedding`. - -.. code-block:: python - - data = fluid.layers.data(name='ids', shape=[1], dtype='int64') - fc = fluid.layers.embedding(input=data, size=[dict_size, 16], is_sparse=True) - -Among the parameters above: - -- :code:`is_sparse`: Use sparse updates to configure embedding. If the dict_size of embedding is large but the number of data are very small each time, it is recommended to use the sparse update method. - - -Improve data IO speed -============================== - -To improve the CPU's distributed training speed, you can first consider using the dataset API as data reader. Dataset is a multi producer and multi consumer data reading method. By default, data reading thread and training thread are coupled. In multi-threaded training, dataset shows a high performance advantage. - -Refer to this page for API introduction: https://www.paddlepaddle.org.cn/documentation/docs/en/api/dataset/QueueDataset.html - -Combined with the actual model CTR-DNN, you can learn more about how to use dataset: https://github.com/PaddlePaddle/models/tree/release/1.7/PaddleRec/ctr/dnn - -Using :code:`train_from_dataset` for network training. - -.. code-block:: python - - dataset = fluid.DatasetFactory().create_dataset() - exe = fluid.Executor(fluid.CPUPlace()) - exe.run(fluid.default_startup_program()) - exe.train_from_dataset(program=fluid.default_main_program(),dataset=dataset) - - -Change distributed training strategy -============================== - -The core of improving CPU distributed training speed is to choose appropriate distributed training strategy, such as defining communication strategy, compiling strategy, executing strategy and so on. PaddlePaddle released :code:`DistributedStrategy` API in V1.7 version , which can be very flexible and convenient to specify distributed operation strategy. - -First, we need to introduce relevant libraries into the code: - -.. code-block:: python - - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet - import paddle.fluid.incubate.fleet.base.role_maker as role_maker - from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy_factory import DistributedStrategyFactory - -At present, there are four kinds of training strategies: synchronous training, asynchronous, half asynchronous training and GEO training. - - -The default configuration of the above policy is introduced by the following code: - -.. code-block:: python - - # step1: get distributed strategy - # Sync - strategy = DistributedStrategyFactory.create_sync_strategy() - # Half-Async - strategy = DistributedStrategyFactory.create_half_async_strategy() - # Async - strategy = DistributedStrategyFactory.create_async_strategy() - # GEO - strategy = DistributedStrategyFactory.create_geo_strategy(update_frequency=400) - - # step2: define role of node - role = role_maker.PaddleCloudRoleMaker() - fleet.init(role) - - # step3: get distributed training program - optimizer = fluid.optimizer.SGD(learning_rate) # 以 SGD 优化器为例 - optimizer = fleet.distributed_optimizer(optimizer, strategy) - optimizer.minimize(loss) - - # step4.1: run parameter server node - if fleet.is_server(): - fleet.init_server() - fleet.run_server() - - # step4.2: run worker node - elif fleet.is_worker(): - fleet.init_worker() - exe.run(fleet.startup_program) - # Do training - exe.run(fleet.main_program) - fleet.stop_worker() - -PaddlePaddle supports adjusting the details of the training strategy: - -- The build_strategy and exec_strategy which used to create compiled_program can generate from strategy: - -.. code-block:: python - - compiled_program = fluid.compiler.CompiledProgram(fleet.main_program).with_data_parallel( - loss_name=loss.name, - build_strategy=strategy.get_build_strategy(), - exec_strategy=strategy.get_execute_strategy()) - - -- Training strategy details can be customized, Paddlepaddle supports customized configuration of distributetranspierconfig, trainerruntimeconfig, serverruntimeconfig, fluid.executionstrategy and fluid.buildstrategy. Take distributetranspillerconfig as an example. The modification method is as follows: - -.. code-block:: python - - strategy = DistributedStrategyFactory.create_sync_strategy() - - # Mode 1 (recommended)： - config = strategy.get_program_config() - config.min_block_size = 81920 - - - # Mode 2 - config = DistributeTranspilerConfig() - config.min_block_size = 81920 - # config = dict() - # config['min_block_size'] = 81920 - strategy.set_program_config(config) diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/dist_training_gpu.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/dist_training_gpu.rst deleted file mode 100644 index 32188831ca9..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/dist_training_gpu.rst +++ /dev/null @@ -1,133 +0,0 @@ -.. _best_practice_dist_training_gpu: - -##################### -分布式 GPU 训练优秀实践 -##################### - -开始优化您的 GPU 分布式训练任务 ---------------------------- - -PaddlePaddle Fluid 支持在现代 GPU [#]_ 服务器集群上完成高性能分布式训练。通常可以通过以下方法优化在多机多卡环境训练性能，建议在进行性能优化时，检查每项优化点并验证对应提升，从而提升最终的性能。 - -一个简单的验证当前的训练程序是否需要进一步优化性能的方法，是查看 GPU 的计算利用率 [#]_ ，通常用 :code:`nvidia-smi` 命令查看。如果 GPU 利用率较低，则可能存在较大的优化空间。下面主要从数据准备、训练策略设置和训练方式三个方面介绍 GPU 分布式训练中常用的优化方法。 - -1、数据准备 -=========== - -数据读取的优化在 GPU 训练中至关重要，尤其在不断增加 batch_size 提升吞吐时，计算对 reader 性能会有更高对要求，优化 reader 性能需要考虑的点包括： - - - 使用 :code:`DataLoader` 。参考 `这里 `_ 使用 DataLoader，并建议开启 :code:`use_double_buffer` 。 - - reader 返回 uint8 类型数据。图片在解码后一般会以 uint8 类型存储，如果在 reader 中转换成 float 类型数据，会将数据体积扩大 4 倍。直接返回 uint8 数据，然后在 GPU 上转化成 float 类型进行训练可以提升数据读取效率。 - - 减少 reader 初始化时间 (infinite read)。在训练任务开始执行第一轮训练时，reader 开始不断异步地从磁盘或其他存储中读取数据并执行预处理，然后将处理好的数据填充到队列中供计算使用。从 0 开始填充这个队列直到数据可以源源不断供给计算，需要一定时间的预热。所以，如果每轮训练都重新填充队列，会产生一些时间的开销。所以，在使用 DataLoader 时，可以让 reader 函数不断地产生数据，直到训练循环结束： - - .. code-block:: python - :linenos: - - def infinite_reader(file_path): - while True: - with open(file_path) as fn: - for line in fn: - yield process(line) - - def train(): - ... - for pass_id in xrange(NUM_PASSES): - if pass_id == 0: - data_loader.start() - for batch_id in (iters_per_pass): - exe.run() - data_loader.reset() - - -另外，可以使用 DALI 库提升数据处理性能。DALI 是 NVIDIA 开发的数据加载库，更多内容请参考 `官网文档 `_ 。飞桨中如何结合使用 DALI 库请参考 `使用示例 `_ 。 - -2、训练策略设置 -=========== - -训练参数设置表 - -.. csv-table:: - :header: "选项", "类型", "默认值", "说明" - :widths: 3, 3, 3, 5 - - ":code:`num_threads`", "int", "1", "CPU 线程数" - ":code:`nccl_comm_num`", "int", "1", "nccl 通信器数量" - ":code:`fuse_all_reduce_ops`", "bool", "False", "多卡训练时，将 AllReduce 操纵进行融合" - ":code:`use_hierarchical_allreduce` ", "bool", "False", "分级式 reduce" - ":code:`num_iteration_per_drop_scope`", "int", "1", "scope drop 频率，设置每隔几个 batch 的迭代之后执行一次清理 scope" - ":code:`fetch_frequency`", "int", "1", "fetch 的刷新频率" - ":code:`fuse_bn_act_ops`", "bool", "False", "是否开启 batch normalization 和激活函数的融合" - ":code:`fuse_elewise_add_act_ops`", "bool", "False", "是否开启 elementwise add 函数和激活函数的融合" - -说明： - -- 关于设置合适的 CPU 线程数 :code:`num_threads` 和 nccl 通信器数量 :code:`nccl_comm_num` 。PaddlePaddle Fluid 使用“线程池” [#]_ 模型调度并执行 Op，Op 在启动 GPU 计算之前，通常需要 CPU 的协助，然而如果 Op 本身占用时间很小，“线程池”模型下又会带来额外的调度开销。使用多进程模式时，如果神经网络的计算图 [#]_ 节点间有较高的并发度，即使每个进程只在一个 GPU 上运行，使用多个线程可以更大限度的提升 GPU 利用率。nccl 通信器数量 :code:`nccl_comm_num` 可以加快 GPU 之间的通信效率，建议单机设置为 1，多机设置为 2。针对 CPU 线程数 :code:`num_threads` ，建议单机设置为 1，多机设置为 :code:`nccl_comm_num` +1。 -- 关于 AllReduce 融合 :code:`fuse_all_reduce_ops` ，默认情况下会将同一 layer 中参数的梯度的 AllReduce 操作合并成一个，比如对于 :code:`fluid.layers.fc` 中有 Weight 和 Bias 两个参数，打开该选项之后，原本需要两次 AllReduce 操作，现在只用一次 AllReduce 操作。此外，为支持更大粒度的参数梯度融合，Paddle 提供了 :code:`FLAGS_fuse_parameter_memory_size` 和 :code:`FLAGS_fuse_parameter_groups_size` 两个环境变量选项。用户可以指定融合 AllReduce 操作之后，每个 AllReduce 操作的梯度字节数，比如希望每次 AllReduce 调用传输 16MB 的梯度，:code:`export FLAGS_fuse_parameter_memory_size=16` ，经验值为总通信量的十分之一。可以指定每次 AllReduce 操作的最大层数，即到达该层数就进行 AllReduce，如指定 50 层 :code:`export FLAGS_fuse_parameter_groups_size=50` 。注意：目前不支持 sparse 参数梯度。 -- 关于使用分级式 reduce :code:`use_hierarchical_allreduce` 。对于多机模式，针对小数据量的通信，Ring AllReduce 通信效率低，采用 Hierarchical AllReduce 可以解决该问题。 -- 关于降低 scope drop 频率 :code:`num_iteration_per_drop_scope` 和 fetch 频率 :code:`fetch_frequency` 。减少 scope drop 和 fetch 频率，可以减少频繁的变量内存申请、释放和拷贝，从而提升性能。 -- 关于操作融合：通过参数融合可以提升训练性能。 - -设置这些参数可以参考： - -.. code-block:: python - :linenos: - - dist_strategy = DistributedStrategy() - dist_strategy.nccl_comm_num = 2 #建议多机设置为 2，单机设置为 1 - exec_strategy = fluid.ExecutionStrategy() - exe_st.num_threads = 3 #建议多机设置为 nccl_comm_num+1，单机设置为 1 - exec_strategy.num_iteration_per_drop_scope = 30 #scope drop 频率 - dist_strategy.exec_strategy = exec_strategy - dist_strategy.fuse_all_reduce_ops = True #AllReduce 是否融合 - ... - with fluid.program_guard(main_prog, startup_prog): #组网 - params = model.params - optimizer = optimizer_setting(params) - dist_optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) - dist_optimizer.minimize(avg_cost) - ... - for pass_id in range(PASS_NUM): - batch_id = 0 - while True: - if batch_id % fetch_frequency == 0: #fetch 频率 - fetched = exe.run(main_prog, fetch_list) - else: - exe.run([]) - - -3、训练方式 -=========== - -1、Local SGD - -GPU 多机多卡同步训练过程中存在慢 trainer 现象，即每步中训练快的 trainer 的同步通信需要等待训练慢的 trainer。由于每步中慢 trainer 的 rank 具有随机性，因此我们使用局部异步训练的方式——LocalSGD，通过多步异步训练（无通信阻塞）实现慢 trainer 时间均摊，从而提升同步训练性能。Local SGD 训练方式主要有三个参数，分别是： - -.. csv-table:: - :header: "选项", "类型", "可选值", "说明" - :widths: 3, 3, 3, 5 - - ":code:`use_local_sgd`", "bool", "False/True", "是否开启 Local SGD，默认不开启" - ":code:`local_sgd_is_warm_steps`", "int", "大于 0", "训练多少轮之后才使用 Local SGD 方式训练" - ":code:`local_sgd_steps`", "int", "大于 0", "Local SGD 的步长" - -说明： - -- Local SGD 的 warmup 步长 :code:`local_sgd_is_warm_steps` 影响最终模型的泛化能力，一般需要等到模型参数稳定之后在进行 Local SGD 训练，经验值可以将学习率第一次下降时的 epoch 作为 warmup 步长，之后再进行 Local SGD 训练。 -- Local SGD 步长 :code:`local_sgd_steps` ，一般该值越大，通信次数越少，训练速度越快，但随之而来的时模型精度下降。经验值设置为 2 或者 4。 - -具体的 Local SGD 的训练代码可以参考：https://github.com/PaddlePaddle/PaddleFleetX/tree/old_develop/deprecated/examples/local_sgd/resnet - - -2、使用混合精度训练 - -V100 GPU 提供了 `Tensor Core `_ 可以在混合精度计算场景极大的提升性能。使用混合精度计算的例子可以参考：https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#using-mixed-precision-training - -目前 Paddle 只提供在两个模型（ResNet, BERT）的混合精度计算实现并支持 static loss scaling，其他模型使用混合精度也可以参考以上的实现完成验证。 - -附录 ----- - -.. [#] 现代 GPU：指至少支持运行 `CUDA `_ 版本 7.5 以上的 GPU -.. [#] GPU 利用率：这里指 GPU 计算能力被使用部分所占的百分比 -.. [#] https://en.wikipedia.org/wiki/Thread_pool -.. [#] https://en.wikipedia.org/wiki/Data-flow_diagram diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_low_bandwidth_dgc.md b/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_low_bandwidth_dgc.md deleted file mode 100644 index ef1017c61c4..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_low_bandwidth_dgc.md +++ /dev/null @@ -1,123 +0,0 @@ -# 低配网络的分布式 GPU 训练 - -## 1. 背景 - 大规模分布式训练需要较高的网络带宽以便进行梯度的聚合更新，这限制了多节点训练时的可扩展性同时也需要昂贵的高带宽设备。在低带宽云网络等环境下进行分布式训练会变得更加糟糕。现有[Deep Gradient Compression](https://arxiv.org/abs/1712.01887)研究表明，分布式 SGD 中有 99.9%的梯度交换都是冗余的，可以使用深度梯度压缩选择重要梯度进行通信来减少通信量，降低对通信带宽的依赖。Paddle 目前实现了 DGC 的稀疏通信方式，可有效在低配网络下进行 GPU 分布式训练。下面将介绍 DGC 稀疏通信方式的使用方法、适用场景及基本原理。 - -## 2. 使用方法 -`注意：使用 DGC 请使用 1.6.2 及其之后版本，之前版本存在有若干 bug。` -DGC 稀疏通信算法以 DGCMomentumOptimizer 接口的形式提供，目前只支持 GPU 多卡及 GPU 多机分布式，由于现有 fuse 策略会造成 DGC 失效，所以使用 DGC 时需设置`strategy.fuse_all_reduce_ops=False`关闭 fuse。DGC 只支持 Momentum 优化器，使用时把当前代码中的 Momentum 优化器替换为 DGCMomentumOptimizer，并添加 DGC 所需参数即可。如下代码所示，其中 rampup_begin_step 表示从第几步开始使用 DGC，更详细参数可见[api 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/optimizer_cn/DGCMomentumOptimizer_cn.html#dgcmomentumoptimizer)。 -``` python -import paddle.fluid as fluid -# optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9) -# 替换 Momentum 优化器，添加 DGC 所需参数 -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=0) -optimizer.minimize(cost) -``` -在 fleet 中我们提供了[DGC 的示例](https://github.com/PaddlePaddle/PaddleFleetX/tree/old_develop/deprecated/examples/dgc_example)。示例中以数字手写体识别为例，将程序移植为分布式版本（注：DGC 亦支持单机多卡），再加上 DGC 优化器。可参照此示例将单机单卡程序迁移到 DGC。在单机单卡迁移到 DGC 过程中，一般需要先对齐多机 Momentum 的精度，再对齐 DGC 的精度。 - -## 3. 调参&适用场景 -### 3.1 预热调参 -对于正常的训练，使用 DGC 一般需进行预热训练，否则可能会有精度损失。如下图是 ResNet50 模型 Imagenet 数据集的训练结果，未进行预热训练的 DGC 最终损失了约 0.3%的精度。 -
-![DGC Resnet50 acc1](images/dgc_resnet50_acc1.png) -
- -预热训练调参可参照论文的设置。对图像分类，论文在 Cifar10 和 ImageNet 数据集上共 164 和 90 个 epochs 的训练中都采用了 4 个 epochs 的预热训练。在语言模型 PTB 数据集上，在共 40 个 epochs 的训练中选择了 1 个 epoch 进行预热训练。在语音识别 AN4 数据集上，80 个 epochs 中选择 1 个 epoch 进行预热训练。 -论文中使用了 75%, 93.75%, 98.4375%, 99.6%, 99.9%稀疏度逐渐提升的策略。由于 paddle 稀疏梯度聚合通信使用了 AllGather，通信量会随卡数增加而增长，所以在卡数较多时不推荐较低稀疏度的预热训练。如 75%稀疏度时每张卡会选择 25%的梯度进行通信，卡数为 32 时通信量是正常 dense 通信的 32\*(1-0.75)=8 倍，所以前几个 epoch 使用正常的 dense 通信为佳。可参照如下写法 -``` python -# 1. 以 1252 个 step 为一个 epoch，前 2 个 epochs 使用正常 dense 通信，后 3 个 epochs 逐步提升稀疏度为 99.9% -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=1252*2, - rampup_step=1252*3, sparsity=[0.984375, 0.996, 0.999]) -# 2. 前面 4 个 epochs 都使用 dense 通信，之后默认 0.999 稀疏度运行 -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=1252*4) -``` -对于 Fine-tuning 训练，现测试可无需预热训练，从第 0 个 epoch 直接使用 DGC 即可。 -``` python -# 从第 0 步开始 DGC 稀疏通信 -optimizer = fluid.optimizer.DGCMomentumOptimizer( - learning_rate=0.001, momentum=0.9, rampup_begin_step=0) -``` -### 3.2 适用场景 -DGC 稀疏通信在低带宽通信瓶颈时会有较大的性能提升，但在单机多卡及 RDMA 网络通信并非瓶颈情况下，并不会带来性能上的提升。同时由于 AllGather 的通信量会随卡数的增多而增大，所以 DGC 的多机训练规模也不宜过大。故 DGC 适用于低配网络，同时节点规模不宜过大，如>128 张卡。在云网络或高带宽网络设备昂贵时，DGC 可有效降低训练成本。 - -## 4. 原理 -本节原理部分基本来自[Deep Gradient Compression](https://arxiv.org/abs/1712.01887)论文，本文进行了部分理解翻译，英文较好者建议直接阅读论文。 -### 4.1 梯度稀疏 -DGC 的基本思路是通过只传送重要梯度，即只发送大于给定阈值的梯度来减少通信带宽的使用。为避免信息的丢失，DGC 会将剩余梯度在局部累加起来，最终这些梯度会累加大到足以传输。 -换个角度，从理论依据上来看，局部梯度累加等同于随时间推移增加 batch size，（DGC 相当于每一个梯度有自己的 batch size）。设定 $F(w)$ 为需要优化的 loss 函数，则有着 N 个训练节点的同步分布式 SGD 更新公式如下 -$$ -F(w)=\\frac{1}{\|\\chi\|}\\sum\_{x\\in\\chi}f(x, w), \\qquad w\_{t+1}=w\_{t}-\\eta\\frac{1}{N b}\\sum\_{k=0}^{N}\\sum\_{x\\in\\mathcal{B}\_{k,t}}\\nabla f\\left(x, w\_{t}\\right) \\tag{1} -$$ -其中$\chi$是训练集，$w$是网络权值，$f(x, w)$是每个样本$x \in \chi$的 loss，$\eta$是学习率，N 是训练节点个数，$\mathcal{B}_{k, t}$代表第$k$个节点在第$t$个迭代时的 minibatch，大小为 b。 -考虑权重的第 i 个值，在 T 次迭代后，可获得 -$$ -w\_{t+T}^{(i)}=w\_{t}^{(i)}-\\eta T \\cdot \\frac{1}{N b T} \\sum\_{k=1}^{N}\\left(\\sum\_{\\tau=0}^{T-1} \\sum\_{x \\in \\mathcal{B}\_{k, t+\\tau}} \\nabla^{(i)} f\\left(x, w\_{t+\\tau}\\right)\\right) \\tag{2} -$$ -等式 2 表明局部梯度累加可以被认为 batch size 从$Nb$增大为$NbT$，其中 T 是$w^{(i)}$两次更新的稀疏通信间隔。 -### 4.2 局部梯度累加改进 -正常情况，稀疏更新会严重影响收敛性。DGC 中采用动量修正(Momentum Correction)和局部梯度裁减(local gradient clipping)来解决这个问题。 -#### 4.2.1 动量修正 -有着 N 个节点分布式训练中 vanilla momentum SGD 公式， -$$ -u\_{t}=m u\_{t-1}+\\sum\_{k=1}^{N}\\left(\\nabla\_{k, t}\\right), \\quad w\_{t+1}=w\_{t}-\\eta u\_{t} \\tag{3} -$$ -其中$m$是动量因子，$N$是节点数，$\nabla_{k, t}=\frac{1}{N b} \sum_{x \in \mathcal{B}_{k, t}} \nabla f\left(x, w_{t}\right)$。 -考虑第 i 个权重$w^{(i)}$，在 T 次迭代后，权重更新公式如下， -$$ -w\_{t+T}^{(i)}=w\_{t}^{(i)}-\\eta\\left[\\cdots+\\left(\\sum\_{\\tau=0}^{T-2} m^{\\tau}\\right) \\nabla\_{k, t+1}^{(i)}+\\left(\\sum\_{\\tau=0}^{T-1} m^{\\tau}\\right) \\nabla\_{k, t}^{(i)}\\right] \\tag{4} -$$ -如果直接应用动量 SGD 到稀疏梯度更新中，则有公式， -$$ -v_{k, t}=v_{k, t-1}+\\nabla_{k, t}, \\quad u_{t}=m u_{t-1}+\\sum_{k=1}^{N} \\operatorname{sparse}\\left(v_{k, t}\\right), \\quad w_{t+1}=w_{t}-\\eta u_{t} \\tag{5} -$$ -其中$v_k$是训练节点 k 上的局部梯度累加项，一旦$v_k$大于某一阈值，则会在第二项中压缩梯度进行动量更新，并使用 sparse()函数获得 mask 清空大于阈值的梯度。 -$w^{(i)}$在 T 次稀疏更新后的权重为, -$$ -w_{t+T}^{(i)}=w_{t}^{(i)}-\\eta\\left(\\cdots+\\nabla_{k, t+1}^{(i)}+\\nabla_{k, t}^{(i)}\\right) \\tag{6} -$$ -相比传统动量 SGD，方程 6 缺失了累积衰减因子$\sum_{\tau=0}^{T-1} m^{\tau}$，会导致收敛精度的损失。如下图 A，正常梯度更新从 A 点到 B 点，但是方程 6 则从 A 点到 C 点。当稀疏度很高时，会显著降低模型性能，所以需要在方程 5 基础上对梯度进行修正。 -
- - -
-若将方程 3 中速度项$u_t$当作“梯度”，则方程 3 第二项可认为是在”梯度“$u_t$上应用传统 SGD，前面已经证明了局部梯度累加在传统 SGD 上是有效的。因此，可以使用方程 3 局部累加速度项$u_t$而非累加真实的梯度$\nabla_{k, t}$来修正方程 5， -$$ -u_{k, t}=m u_{k, t-1}+\\nabla_{k, t}, \\quad v_{k, t}=v_{k, t-1}+u_{k, t}, \\quad w_{t+1}=w_{t}-\\eta \\sum_{k=1}^{N} \\operatorname{sparse}\\left(v_{k, t}\\right) \\tag{7} -$$ -修正后，如上图(b)，方程可正常从 A 点到 B 点。除了传统动量方程修正，论文还给出了 Nesterov 动量 SGD 的修正方程。 -#### 4.2.2 局部梯度修剪 -梯度修剪是防止梯度爆炸的常用方法。这方法由 Pascanu 等人在 2013 年提出，当梯度的 l2-norms 和大于给定阈值时，就对梯度 rescale。正常梯度修剪在梯度聚合后使用，而 DGC 因为每个节点独立的进行局部梯度累加，所以 DGC 在使用$G_t$累加前对其进行局部梯度修剪。阈值缩放为原来的$N^{-1/2}$ -$$ -thr_{G^{k}}=N^{-1 / 2} \\cdot thr_{G} \\tag{8} -$$ -### 4.3 克服迟滞效应 -因为推迟了较小梯度更新权重的时间，所以会有权重陈旧性问题。稀疏度为 99.9%时大部分参数需 600 到 1000 步更新一次。迟滞效应会减缓收敛并降低模型精度。DGC 中采用动量因子掩藏和预热训练来解决这问题。 -#### 4.3.1 动量因子掩藏 -DGC 中使用下面方程来掩藏动量因子减缓陈旧性问题。 -$$ -Mask \\leftarrow\\left|v_{k, t}\\right|>t h r, \\quad v_{k, t} \\leftarrow v_{k, t} \\odot \\neg Mask, \\quad u_{k, t} \\leftarrow u_{k, t} \\odot \\neg Mask \\tag{9} -$$ -此掩码可以停止延迟梯度产生的动量，防止陈旧梯度把权重引入错误的方向。 - -#### 4.3.2 预热训练 -在训练初期，梯度变动剧烈，需要及时更新权重，此时迟滞效应影响会很大。为此 DGC 采用预热训练的方法，在预热期间使用更小的学习率来减缓网络的变化速度，并使用较小的稀疏度来减少需推迟更新的梯度数量。预热期间会线性增大学习率，指数型增加稀疏度到最终值。 - -### 4.4 正则化(Weight Decay)项修正 -Paddle 框架以 Weight Decay 的形式实现正则化。以 L2Decay 为例，公式(3)中传统 momentum 添加 weight decay 后公式为 -$$ -G_{t}=\\sum_{k=1}^{N}\\left(\\nabla_{k, t}\\right)+\\lambda w_{t}, \\quad u_{t}=m u_{t-1}+G_{t}, \\quad w_{t+1}=w_{t}-\\eta u_{t} \\tag{10} -$$ -其中$\lambda$为 Weight Decay 系数，$G_{t}$为添加 L2Decay 项之后的聚合梯度。由于在公式 7 中进行了局部动量修正，所以按照相同思路在局部梯度上运用修正的 Weight Decay 项。如下公式在局部梯度上添加局部 Weight Decay 项即可。 -$$ -\\nabla_{k, t}=\\nabla_{k, t}+\\frac{\\lambda}{N} w_{t} \\tag{11} -$$ -在模型实际训练中，通常会设置 weight decay 的系数$\lambda=10^{-4}$，在卡数较多如 4 机 32 卡的情况下局部 weight decay 系数为$\frac{\lambda}{N}=\frac{10^{-4}}{32}=3.125*10^{-6}$，在数值精度上偏低，测试训练时会损失一定精度。为此还需对局部 weight decay 项进行数值修正。如下公式， -$$ -\\nabla_{k, t}^{'}=N \\nabla_{k, t}+\\lambda w_{t}, \\quad -G_{t}^{'}=\\sum_{k=1}^{N}\\left(\\nabla_{k, t}^{'}\\right)=N\\sum_{k=1}^{N}\\left(\\nabla_{k, t}\\right)+N\\lambda w_{t}, \\quad -G_{t}=\\frac{G_{t}^{'}}{N}=\\sum_{k=1}^{N}\\left(\\nabla_{k, t}\\right)+\\lambda w_{t} \\tag{12} -$$ -具体做法为对局部梯度乘以卡数求得$\nabla_{k, t}^{'}$，此时$\lambda$项则无需除以卡数，聚合梯度求得$G_{t}^{'}$再对聚合梯度除以卡数得到$G_{t}$即可。 diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute.rst deleted file mode 100644 index ad16707dd87..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute.rst +++ /dev/null @@ -1,160 +0,0 @@ - -重计算：大 Batch 训练特性 -============= - -背景 ---------- - -随着训练数据规模的逐渐增加，训练更大、更深的深度学习模型成为一个主流趋势。目前的深度学习模型训练，通常要求保留前向计算的隐层结果，并且需要保存结果的数量会随着模型层数的增加线性增加，这对于目前能够使用的 AI 芯片的内存大小是个挑战。Forward Recomputation Backpropagation（FRB）可以在额外增加少量计算的情况下，显著增加模型的层数和宽度，同时也可以显著提升模型训练的 batch 大小。 - -原理 ---------- - -我们知道，深度学习网络的一次训练迭代包含三个步骤： - -- **前向计算**：运行前向算子(Operator) 来计算中间隐层(Variable)的值 -- **反向计算**：运行反向算子来计算参数(Parameter)的梯度 -- **优化**：应用优化算法以更新参数值 - -在前向计算过程中，前向算子会输出大量的中间计算结果，在 Paddle 中，使用 -Variable 来存储这些隐层的中间结果。当模型层数加深时，其数量可达成千上万个， -占据大量的内存。Paddle 的 `显存回收机制 `_ -会及时清除无用的中间结果，以节省存储。 -然而，有些中间结果是反向算子的输入，这些 Variable 必须存储在内存中，直到相应的反向算子计算完毕。 - -举个简单的例子, 我们定义一个由 mul 算子构成的网络，其前向计算为： - -.. math:: - - y = W_1 * x - - z = W_2 * y - -其中 :math:`x, y, z` 为向量， :math:`W_1, W_2` 为矩阵。容易知道，求 :math:`W_2` 梯度的反向计算为： - -.. math:: - W_{2}^{'} = z^{'} / y - -可以看到反向计算中用到了前向计算生成的变量 :math:`y` ，因此变量 :math:`y` 必须存储在内存中，直到这个反向算子计算完毕。当模型加深时，我们会有大量的“ :math:`y` ”，占据了大量的内存。 - -Forward Recomputation Backpropagation（FRB）的思想是将深度学习网络切分为 k 个部分（segments）。对每个 segment 而言：前向计算时，除了小部分必须存储在内存中的 Variable 外(我们后续会讨论这些特殊 Variable)，其他中间结果都将被删除；在反向计算中，首先重新计算一遍前向算子，以获得中间结果，再运行反向算子。简而言之，FRB 和普通的网络迭代相比，多计算了一遍前向算子。 - -我们把切分网络的变量叫做 checkpoints。 -那么问题来了，如何选择 checkpoints 呢？自从 FRB 方法提出以来 \ :sup:`[1], [2]`，大量学者在研究这一关键问题。 -我们知道深度学习网络通常是由一个个模块串联得到的，比如 ResNet-50 由 16 个 block 串联而成， -Bert-Large 由 24 个 transformer 串联而成，以两个子模块中间的变量作为切分点就是一个很好的选择。 -对于非串联的网络（比如含有大量 shortcut 结构的网络），FRB 也支持对其做切分， -只是可能多耗费一点内存（用于存储 shortcut 的 Variable）。 -Mitsuru Kusumoto \ :sup:`[3]` 等提出了一种基于动态规划的算法， -可以根据指定的内存自动搜索合适的 checkpoints，支持各种各样的网络结构。 - -下图是由 4 个 fc Layer、3 个 relu Layer、1 个 sigmoid Layer 和 1 个 log-loss Layer 串联而成的一个网络：最左侧为其前向计算流程、中间是普通的前向计算和反向计算流程、最右侧为添加 FRB 后的前向计算和反向计算流程。其中方框代表算子(Operator)，红点代表前向计算的中间结果、蓝点代表 checkpoints。 - -.. image:: images/recompute.png - -注：该例子完整代码位于 `source `_ - -添加 FRB 后，前向计算中需要存储的中间 Variable 从 4 个(红点)变为 2 个(蓝点)， -从而节省了这部分内存。当然了，重计算的部分也产生了新的中间变量， -这就需要根据实际情况来做权衡了。这个例子里的网络比较浅，通常来讲， -对层数较深的网络，FRB 节省的内存要远多于新增加的内存。 - -使用方法 ---------- - -我们实现了基于 Paddle 的 FRB 算法，叫做 RecomputeOptimizer， -您可以根据其 `源码 `_ -与 -`文档 `_ -更深入地了解这一算法。我们为用户提供了两个使用 RecomputeOptimizer 的方法: -直接调用与 Fleet API 中使用。在单机单卡或者 CPU 训练中建议您直接调用 RecomputeOptimizer， -在多卡训练或者多机训练任务上建议您在 Fleet API 中使用 Recompute。 - -**1. 直接调用** - -直接调用 RecomputeOptimizer 非常简单，首先要定义一个经典的 Optimizer，比如 Adam； -然后在外面包一层 RecomputeOptimizer；最后设置 checkpoints 即可。 - -.. code-block:: python - - import paddle.fluid as fluid - # 定义网络 - def mlp(input_x, input_y, hid_dim=128, label_dim=2): - print(input_x) - fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) - prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') - cost = fluid.layers.cross_entropy(input=prediction, label=input_y) - sum_cost = fluid.layers.reduce_mean(cost) - return sum_cost, fc_1, prediction - input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') - input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') - cost, fc_1, pred = mlp(input_x, input_y) - # 定义 RecomputeOptimizer - sgd = fluid.optimizer.Adam(learning_rate=0.01) - sgd = fluid.optimizer.RecomputeOptimizer(sgd) - # 设置 checkpoints - sgd._set_checkpoints([fc_1, pred]) - # 运行优化算法 - sgd.minimize(cost) - -Recompute 原则上适用于所有 Optimizer。 - -**2. 在 Fleet API 中使用 Recompute** - -`Fleet API `_ -是基于 Fluid 的分布式计算高层 API。在 Fleet API 中添加 RecomputeOptimizer -仅需要 2 步： - -- 设置 dist_strategy.forward_recompute 为 True； - -- 设置 dist_strategy.recompute_checkpoints。 - -.. code-block:: python - - from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy - dist_strategy = DistributedStrategy() - dist_strategy.forward_recompute = True - dist_strategy.recompute_checkpoints=checkpoints - optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) - optimizer.minimize(loss) - -为了帮助您快速地用 Fleet API 使用 Recompute 任务，我们提供了一些例子， -并且给出了这些例子的计算速度、效果和显存节省情况： - -- 用 Recompute 做 Bert Fine-tuning: `source `_ - -- 用 Recompute 做目标检测：开发中. - -Q&A -------- - -- **是否支持带有随机性的 Op？** - - 目前 Paddle 中带随机性的 Op 有：dropout，Recompute 支持 - dropout Operator，可以保证重计算与初次计算结果保持一致。 - -- **有没有更多 Recompute 的官方例子？** - - 更多 Recompute 的例子将更新在 `examples `_ - 和 `Fleet `_ 库下，欢迎关注。 - -- **有没有添加 checkpoints 的建议？** - - 我们建议将子网络连接部分的变量添加为 checkpoints，即： - 如果一个变量能将网络完全分为前后两部分，那么建议将其加入 checkpoints。 - checkpoints 的数目会影响内存的消耗：如果 checkpoints 很少， - 那么 Recompute 起的作用有限；如果 checkpoints 数量过多， - 那么 checkpoints 本身占用的内存量就较大，内存消耗可能不降反升。 - - 我们后续会添加一个估算内存用量的工具， - 可以对每个 Operator 运算前后的显存用量做可视化， - 帮助用户定位问题。 - -[1] Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin . Training deep nets with sublinear memory cost. -arXiv preprint, arXiv:1604.06174, 2016. - -[2] Audrunas Gruslys , Rémi Munos , Ivo Danihelka , Marc Lanctot , and Alex Graves. Memory efficient -backpropagation through time. In Advances in Neural Information Processing Systems (NIPS), pages 4125 4133, -2016. - -[3] Kusumoto, Mitsuru, et al. "A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation." arXiv preprint arXiv:1905.11722 (2019). diff --git a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute_en.rst b/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute_en.rst deleted file mode 100644 index d9a556bb74c..00000000000 --- a/docs/advanced_guide/performance_improving/multinode_training_improving/gpu_training_with_recompute_en.rst +++ /dev/null @@ -1,196 +0,0 @@ - -Recompute: Training with bigger batch size -============= - -Context ---------- - -As the amount of training data increases, training deeper neural network models becomes more and more popular. Current deep-learning training usually keeps the hidden layer outputs in memory during the forward propagation, -and the number of outputs increases linearly with -the increase of the number of model layers, -which becomes a challenge of the memory size -for common devices. - - -Theory ---------- - -As we know, a training process of a deep-learning network contains 3 steps: - -- **Forward Propagation**：Running forward operators and generate temporary variables as output -- **Backward Propagation**：Running backward operators to compute gradients of parameters -- **Optimization**：Applying optimization algorithm to update parameters - -When the model becomes deeper, the number of temporary variables -generated in the forward propagation process can reach tens -of thousands, occupying a large amount of memory. -The `Garbage Collection mechanism `_ -in Paddle can delete useless variables for the sake of saving memory. -However, some variables serve as inputs of backward operators, -they must be kept in memory until particular operator finish. - -Take a simple example, define a network contains two `mul` operators, -the forward propagation works as follows: - -.. math:: - - y = W_1 * x - - z = W_2 * y - -where :math:`x, y, z` are vectors， :math:`W_1, W_2` are matrix。It is easy to conduct that the gradient of :math:`W_2` is: - -.. math:: - W_{2}^{'} = z^{'} / y - -We can see that :math:`y` is used in the backward propagation process, -thus it must be kept in the memory during the whole forward propagation. -When network grows deeper, more 'y's need to be stored, -adding more requirements to the memory. - -Forward Recomputation Backpropagation(FRB) splits a deep network to k segments. -For each segment, in forward propagation, -most of the temporary variables are erased in time, -except for some special variables (we will talk about that later); -in backward propagation, the forward operators will be recomputed -to get these temporary variables before running backward operators. -In short, FBR runs forward operators twice. - -But how to split the network? A deep learning network usually consists -of connecting modules in series: -ResNet-50 contains 16 blocks and Bert-Large contains 24 transformers. -It is a good choice to treat such modules as segments. -The variables among segments are -called as checkpoints. - -The following picture is a network with 4 fc layers, 3 relu layers, -1 sigmoid layer and 1 log-loss layer in series. -The left column is the forward propagation, -the middle column is the normal backward propagation, -and the right column is the FRB. -Rectangular boxes represent the operators, red dots represent -the intermediate variables in forward computation, blue dots -represent checkpoints and arrows represent the dependencies between operators. - -.. image:: images/recompute.png - -Note: the complete source code of this example: `source `_ - -After applying FBR, the forward computation only needs to store -2 variables (the blue dots) instead of 4 variables (the red -dots), saving the corresponding memories. It is notable that -recomputing operators generate new intermediate variables at the same time, -a trade-off needs to be considered in this situation. -While according to our experiments, -FBR usually saves rather than increase the memory load. - -Usage ---------- - -We have implemented the FRB algorithm named "RecomputeOptimizer" -based on Paddle. More information about this algorithm can -be learned by the `source code `_ -and the -`document `_ -of RecomputeOptimizer. - -There are 2 methods to apply RecomputeOptimizer in your Paddle -program: call RecomputeOptimizer directly or use it with Fleet -API. For single-GPU card training or CPU training, we recommend -directly calling; For multi-GPU training, we -recommend using with Fleet API. - -**1. Directly calling** - -Calling RecomputeOptimizer is very easy: first, define a classic -optimizer, such as Adam; second, wrap it with RecomputeOptimizer; -third, set the checkpoints. - -.. code-block:: python - - import paddle.fluid as fluid - # Define the network - def mlp(input_x, input_y, hid_dim=128, label_dim=2): - print(input_x) - fc_1 = fluid.layers.fc(input=input_x, size=hid_dim) - prediction = fluid.layers.fc(input=[fc_1], size=label_dim, act='softmax') - cost = fluid.layers.cross_entropy(input=prediction, label=input_y) - sum_cost = fluid.layers.reduce_mean(cost) - return sum_cost, fc_1, prediction - input_x = fluid.layers.data(name="x", shape=[32], dtype='float32') - input_y = fluid.layers.data(name="y", shape=[1], dtype='int64') - cost, fc_1, pred = mlp(input_x, input_y) - # define RecomputeOptimizer - sgd = fluid.optimizer.Adam(learning_rate=0.01) - sgd = fluid.optimizer.RecomputeOptimizer(sgd) - # set checkpoints - sgd._set_checkpoints([fc_1, pred]) - # apply optimization - sgd.minimize(cost) - -In principle, recompute is for all kinds of optimizers in Paddle. - -**2. Using Recompute in Fleet API** - -`Fleet API `_ -is a high-level API for distributed training in Fluid. Adding -RecomputeOptimizer to Fluid takes two steps: - -- set dist_strategy.forward_recompute to True - -- set dist_strategy.recompute_checkpoints - -.. code-block:: python - - from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy - dist_strategy = DistributedStrategy() - dist_strategy.forward_recompute = True - dist_strategy.recompute_checkpoints=checkpoints - optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy) - optimizer.minimize(loss) - -We supply some examples of using recompute in Fleet API for users. -We also post corresponding training speed, -test results and memory usages of these examples for reference. - - -- Fine-tuning Bert Large model with recomputing: `source `_ - -- Training object detection models with recomputing：developing. - -Q&A -------- - -- **Does RecomputeOptimizer support operators with random outputs?** - -We currently found that the dropout operator has random results -and RecomputeOptimizer is able to keep the outputs of -first-computation and recomputation consistent. - - -- **Are there more official examples of Recompute?** - - More examples will be updated at `examples `_ -and `Fleet `_ . Feel free to -raise issues if you get any problem with these examples. - -- **How should I set checkpoints?** - -The position of checkpoints is important: -we suggest setting the variable between the sub-model as checkpoints, -that is, set a variable as a checkpoint if it -can separate the network into two parts without short-cut connections. -The number of checkpoints is also important: -too few checkpoints will reduce the memory saved by recomputing while -too many checkpoints will occupy a lot of memory themselves. -We will add a tool to estimate the memory usage with specific checkpoints, -helping users to choose checkpointing variables. - -[1] Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin . Training deep nets with sublinear memory cost. -arXiv preprint, arXiv:1604.06174, 2016. - -[2] Audrunas Gruslys , Rémi Munos , Ivo Danihelka , Marc Lanctot , and Alex Graves. Memory efficient -backpropagation through time. In Advances in Neural Information Processing Systems (NIPS), pages 4125 4133, -2016. - -[3] Kusumoto, Mitsuru, et al. 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ztz>fH3k*sC4%MoPZvtOt2E3dXxCB17cHvo@`MWQK$x1@I3CMm3(sSrPJ7sbaB5aUl zk(}wV6}VI>q`Sz5K!^!=>;x(t^d>qup6Wlrh@OnW$!*1g12YVhwRWgOZ(N-q&9wMu z^fYNvM?EFbRdya>+N~HV6nLw*YCuGU#5$ho%G=ih?`ArFEvE~V>jc6W|4nP%`s_sc b43>ZLdbWM0`Z=>47=Xaj)z4*}Q$iB}?8Sbc diff --git a/docs/dev_guides/api_contributing_guides/new_cpp_op_en.md b/docs/dev_guides/api_contributing_guides/new_cpp_op_en.md deleted file mode 100755 index b9d52690770..00000000000 --- a/docs/dev_guides/api_contributing_guides/new_cpp_op_en.md +++ /dev/null @@ -1,478 +0,0 @@ -# How to write a new operator - - -## Background - -Here are the base types needed. For details, please refer to the design docs. - -- `class OpProtoAndCheckerMaker`: Describes an Operator's input, output, attributes and description, mainly used to interface with Python API. -- `framework::OperatorBase`: Operator (Op)base class. -- `framework::OpKernel`: Base class for Op computation kernel. -- `framework::OperatorWithKernel`: Inherited from OperatorBase, describing an operator with computation kernels. - - -Operators can be categorized into two groups: operator with kernel(s) and operator without kernel(s). An operator with kernel(s) inherits from `OperatorWithKernel` while the one without kernel(s) inherits from `OperatorBase`. This tutorial focuses on implementing operators with kernels. In short, an operator includes the following information: - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Information Where is it defined
OpProtoMake definition `.cc`files, Backward Op does not need an OpProtoMake interface.
Op definition `.cc` files
Kernel implementation The kernel methods shared between CPU and CUDA are defined in `.h` files. CPU-specific kernels live in `.cc` files, while CUDA-specific kernels are implemented in `.cu`files.
Registering the Op Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation.
- - -New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/fluid/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions.** - - -Let's take matrix multiplication operator, [MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc), as an example to introduce the writing of an Operator with Kernel. - - -## Implementing C++ Types - - -### Defining ProtoMaker - -Matrix Multiplication can be written as $Out = X * Y$, meaning that the operation consists of two inputs and one output. - -First, define `ProtoMaker` to describe the Operator's input, output, and additional comments: - -```cpp -class MulOpMaker : public framework::OpProtoAndCheckerMaker { - public: - MulOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "(Tensor), 2D tensor of size (M x K)"); - AddInput("Y", "(Tensor), 2D tensor of size (K x N)"); - AddOutput("Out", "(Tensor), 2D tensor of size (M x N)"); - AddComment(R"DOC( -Two Element Mul Operator. -The equation is: Out = X * Y -)DOC"); - } -}; -``` - -[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc#L76-L127)is inherited from`framework::OpProtoAndCheckerMaker`, consisting of 2 variables in the constructor： - - - `framework::OpProto` stores Operator input and variable attribute, used for generating Python API interfaces. - - `framework::OpAttrChecker` is used to validate variable attributes. - -The constructor utilizes `AddInput` to add input parameter, `AddOutput` to add output parameter, and `AddComment` to add comments for the Op, so that the corresponding information will be added to `OpProto`. - -The code above adds two inputs `X` and `Y` to `MulOp`, an output `Out`, and their corresponding descriptions. Names are given in accordance to Paddle's [naming convention](https://github.com/PaddlePaddle/FluidDoc/blob/release/1.2/doc/fluid/dev/name_convention.md). - - -An additional example [`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/scale_op.cc#L38-L55) is implemented as follows: - - -```cpp -template -class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { - public: - ScaleOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "(Tensor) Input tensor of scale operator."); - AddOutput("Out", "(Tensor) Output tensor of scale operator."); - AddComment(R"DOC( -Scale operator -$$Out = scale*X$$ -)DOC"); - AddAttr("scale", - "(float, default 1.0)" - "The scaling factor of the scale operator.") - .SetDefault(1.0); - } -}; -``` - -Note `AddAttr("scale", "...").SetDefault(1.0);` adds `scale`constant as an attribute, and sets the default value to 1.0. - - -### Defining the GradProtoMaker class - -Each Op must have a corresponding GradProtoMaker. If GradProtoMaker corresponding to the forward Op is not customized, Fluid provides DefaultGradProtoMaker. The default registration will use all input and output, including Input, Output, Output@Grad and so on. Using unnecessary variables will cause waste of memory. -The following example defines ScaleOp's GradProtoMaker. - -```cpp -class ScaleGradMaker : public framework::SingleGradOpDescMaker { - public: - using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; - - std::unique_ptr Apply() const override { - auto *grad_op = new framework::OpDesc(); - grad_op->SetType("scale"); - grad_op->SetInput("X", OutputGrad("Out")); - grad_op->SetOutput("Out", InputGrad("X")); - grad_op->SetAttr("scale", GetAttr("scale")); - return std::unique_ptr(grad_op); - } -}; -``` - -### Defining Operator - -The following code defines the interface for MulOp: - -```cpp -class MulOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(const framework::InferShapeContext &ctx) const override { - //never use Input or Output if you want a to get a LoDTensor. - auto dim0 = ctx.Input("X")->dims(); - auto dim1 = ctx.Input("Y")->dims(); - PADDLE_ENFORCE_EQ(dim0.size(), 2, - "input X(%s) should be a tensor with 2 dims, a matrix", - ctx.op_.Input("X")); - PADDLE_ENFORCE_EQ(dim1.size(), 2, - "input Y(%s) should be a tensor with 2 dims, a matrix", - ctx.op_.Input("Y")); - PADDLE_ENFORCE_EQ( - dim0[1], dim1[0], - "First matrix's width must be equal with second matrix's height."); - ctx.Output("Out")->Resize({dim0[0], dim1[1]}); - } -}; -``` - -[`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/mul_op.cc#L24) is inherited from `OperatorWithKernel`. Its `public` member - -```cpp -using framework::OperatorWithKernel::OperatorWithKernel; -``` - -expresses an operator constructor using base class `OperatorWithKernel`, alternatively written as - -```cpp -MulOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorWithKernel(type, inputs, outputs, attrs) {} -``` - -`InferShape` interface needs to be re-written.`InferShape` is a const method and cannot modify Op's member variables. Its constant member `const framework::InferShapeContext &ctx` can be used to extract input, output, and attributes. Its functions are - - - 1). validate and error out early: it checks input data dimensions and types. - - 2). configures the tensor shape in the output. - -Usually `OpProtoMaker` and `Op` definitions are written in `.cc` files, which also include the registration methods introduced later. - - -### Defining OpKernel - -`MulKernel` is derived from `framework::OpKernel`, which includes the following templates: - -- `typename DeviceContext` denotes device context type. When different devices, namely the CPU and the CUDA, share the same kernel, this template needs to be added. If they don't share kernels, this must not be added. An example of a non-sharing kernel is [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/cross_entropy_op.h#L43). - -- `typename T` denotes data type, such as `float` or `double`. - -`MulKernel` types need to rewrite the interface for `Compute`. - -- `Compute` takes one input parameter: `const framework::ExecutionContext& context`. -- Compared with `InferShapeContext`, `ExecutionContext` includes device types, and can similarly extract input, output, and attribute variables. -- `Compute` function implements the computation logics of an `OpKernel`. - -The input and output of Op can be obtained by `ExecutionContext::Input()` and `ExecutionContext::Output()` respectively. - -**Note:** If the input/output variable type of op is `LoDTensor` (In Fluid, all Tensors are LoDTensor type by default), please write `ExecutionContext::Input()` and `ExecutionContext:: Output()`, do not write `ExecutionContext::Input()` and `ExecutionContext::Output()`. Because if the actual variable type is `SelectedRows`, the `Input()` and `Output()` methods will specialize the `SelectedRows` type to `Tensor`, causing a potential error. - - -`MulKernel`'s implementation of `Compute` is as follows: - -```cpp -template -class MulKernel : public framework::OpKernel { -public: -void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Input("Y"); - auto* Z = context.Output("Out"); - Z->mutable_data(context.GetPlace()); - auto& device_context = context.template device_context(); - math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); -} -}; -``` - -Note that **different devices (CPU, CUDA)share one Op definition; whether or not they share the same `OpKernel` depends on whether functions called by `Compute`can support both devices.** - -`MulOp`'s CPU and CUDA share the same `Kernel`. A non-sharing `OpKernel` example can be seen in [`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/cross_entropy_op.cc). - -To ease the writing of `OpKernel` compute, and for reusing code cross-device, [`Eigen-unsupported Tensor`](https://bitbucket.org) module is used to implement `Compute` interface. To learn about how the Eigen library is used in PaddlePaddle, please see [usage document](https://github.com/PaddlePaddle/FluidDoc/blob/release/1.2/doc/fluid/dev/use_eigen_cn.md). - - -This concludes the forward implementation of an operator. Next its operation and kernel need to be registered in a `.cc` file. - -The definition of its corresponding backward operator, if applicable, is similar to that of an forward operator. **Note that a backward operator does not include a `ProtoMaker`**. - - - -### Registering Operator and OpKernel - -- In `.cc` files, register forward and backward operator classes and the CPU kernel. - - ```cpp - namespace ops = paddle::operators; - REGISTER_OPERATOR(mul, ops::MulOp, ops::MulOpMaker, - paddle::framework::DefaultGradOpDescMaker) - REGISTER_OPERATOR(mul_grad, ops::MulGradOp) - REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); - REGISTER_OP_CPU_KERNEL(mul_grad, - ops::MulGradKernel); - ``` - - In that code block, - - - `REGISTER_OPERATOR` registers the `ops::MulOp` class, with the type named `mul`. Its `ProtoMaker` is `ops::MulOpMaker`. Register `ops::MulOpGrad` as type named `mul_grad`. - - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specializes template parameters as type `paddle::platform::CPUPlace` and `float`, and also registers `ops::MulGradKernel`. - - -- Registering CUDA Kernel in `.cu` files - - Note that if CUDA Kernel is implemented using the `Eigen unsupported` module, then on top of `.cu`, a macro definition `#define EIGEN_USE_GPU` is needed, such as - - ```cpp - // if use Eigen unsupported module before include head files - #define EIGEN_USE_GPU - - namespace ops = paddle::operators; - REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel); - REGISTER_OP_CUDA_KERNEL(mul_grad, - ops::MulGradKernel); - - ``` - - -### Compilation - -In folder `build/paddle/fluid/operators`, run the following commands to compile. - -``` -make mul_op -``` - - -## Python Binding - -The system will automatically bind the new op to Python and link it to a generated library. - - -## Unit Tests - -Unit tests for an operator include - -1. comparing a forward operator's implementations on different devices (CPU, CUDA) - -2. comparing a backward operator's implementation on different devices (CPU, CUDA) - -3. a gradient test for the backward operator. - -Here, we introduce the [unit tests for `MulOp`](https://github.com/PaddlePaddle/Paddle/tree/develop/test/legacy_test/test_mul_op.py). - - - -### Unit Test for Forward Operators - -The Op unit test is inherited from `OpTest`. More specific unit tests are done in `TestMulOp`. To test the Operator, you need to: - -1. Define input, output, and related property parameters in the `setUp` function. -2. Generate random input data. -3. Implement the same calculation logic as the forward operator in the Python script to get the output, which is to be compared with the output of the forward operator calculation. -4. The backward calculation has been automatically integrated into the test framework and the corresponding interface can be called directly. - - ```python - import unittest - import numpy as np - from op_test import OpTest - - - class TestMulOp(OpTest): - def setUp(self): - self.op_type = "mul" - self.inputs = { - 'X': np.random.random((32, 84)).astype("float32"), - 'Y': np.random.random((84, 100)).astype("float32") - } - self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} - - def test_check_output(self): - self.check_output() - - def test_check_grad_normal(self): - self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - - def test_check_grad_ingore_x(self): - self.check_grad( - ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - - def test_check_grad_ingore_y(self): - self.check_grad( - ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) - ``` - - -The code above first loads required packages. In addition, we have - -- `self.op_type = "mul" ` defines the type that is identical to what the operator's registered type. -- `self.inputs` defines input, with type `numpy.array` and initializes it. -- `self.outputs` defines output and completes the same operator computation in the Python script, and returns its result from the Python script. - - -### Unit Test for Backward Operators - -In the backward operator test: - -- `check_grad` is called in `test_check_grad_normal` to use numerical methods to detect gradient correctness and stability. -- The first parameter `["X", "Y"]` : specifies gradient check for the input variables `X`, `Y`. -- The second parameter `"Out"` : specifies the final output target variable `Out` of the forward network. -- The third parameter `max_relative_error`: specifies the maximum error value that can be tolerated when checking gradients. -- The `test_check_grad_ingore_x` and `test_check_grad_ingore_y` branches are used to test cases where only one input gradient needs to be calculated. - - - -### Compiling and Running - - -Any new unit testing file of the format `test_*.py` added to the directory `test/legacy_test` is automatically added to the project to compile. - -Note that **running unit tests requires compiling the entire project** and requires compiling with flag `WITH_TESTING` on i.e. `cmake paddle_dir -DWITH_TESTING=ON`. - -After successfully compiling the project, run the following command to run unit tests: - -```bash -make test ARGS="-R test_mul_op -V" -``` - -Or, - -```bash -ctest -R test_mul_op -``` - - - -## Remarks - -- The type with which an operator is registered needs to be identical to the Op's name. Registering `REGISTER_OPERATOR(B, ...)` in `A_op.cc` will cause unit testing failures. -- If the operator does not implement a CUDA kernel, please refrain from creating an empty `*_op.cu` file, or else unit tests will fail. -- If multiple operators rely on some shared methods, a file NOT named `*_op.*` can be created to store them, such as `gather.h`. - - - - - -### PADDLE_ENFORCE Usage Note - -To check the validity of data when implementing Op, you need to use macro definitions such as PADDLE_ENFORCE and PADDLE_ENFORCE_EQ. The basic format is as follows: - -``` -PADDLE_ENFORCE (expression, error message) -PADDLE_ENFORCE_EQ (comparison object A, comparison object B, error message) -``` - -If the expression is true, or the comparison object A=B, the check will be passed, otherwise the program will be terminated and the corresponding error message will be fed back to the user. -In order to ensure that the feedbacks are user-friendly and easy to understand, developers need to pay attention to how to use them. - - - -#### General Principles - -Any place where PADDLE_ENFORCE and PADDLE_ENFORCE_EQ are used must have a properly detailed explanation of the comments! **Error message** can't be empty! - - - -#### Error Message Standard - -1. [required] Where does it go wrong? Why is it wrong? - - - For example: `ValueError: Mismatched label shape` - -2. [optional] What is the expected input? What is the actual input? - - - For example: `Expected labels dimension=1. Received 4.` - -3. [optional] Can you come up with a suggestion? - - - For example: `Suggested Fix: If your classifier expects one-hot encoding label, check your n_classes argument to the estimatorand/or the shape of your label.Otherwise, check the shape of your label.` - -If it is not necessary or concise description is enough to clearly express the above points, just write based on actual needs. - - - -#### Typical Problems - - -1.No error message exists or error message is too short to provide effective notification to the user. - - Problem example 1: Absent message - ``` - PADDLE_ENFORCE(ctx->HasInput("X"), ""); - ``` - Problem example 2: The prompt message is too short - ``` - PADDLE_ENFORCE(i != nullptr, "i must be set"); // What is i? - ``` - -2.Using developer-defined variable abbreviations in error messages is not easy to understand. - - Example of the problem: - ``` - PADDLE_ENFORCE(forward_pd != nullptr, - "Fail to find eltwise_fwd_pd in device context"); //eltwise_fwd_pduser may not be understood - ``` - -3.The OP internally calls the illegal interface: If Op appears inside Output = ShareDataWith(Input) - Example of the problem: - ```cpp - auto *out = ctx.Output("Out"); - auto *in = ctx.Input("X"); - out->ShareDataWith(*in); - ``` - - If there is Output = ShareDataWith(Input) inside Op, it will equivalently indicate a hidden edge in the operator graph, which connects Input and Output. This edge cannot be expressed in graph analysis, causing error based on graph optimization. - -4.Performance of OP implementation. It called eigen's broadcast, chop and other operations, the performance will be over several times worse than the handwritten cuda kernel. At this point, the implementation of cpu can reuse eigen, and the gpu implementation can implement cuda kernel. - - - - -#### Special Instructions for OP InferShape Check Message - -- Check input and output variables, please follow the following format -`Input(variable name) of OP name operator should not be null.` - - The correct example: - ``` - PADDLE_ENFORCE(ctx->HasInput("Input"), - "Input(Input) of LSTMP operator should not be null."); - ``` - -- Backward Op input and output check, to write the name of the backward Op - - The correct example: - ``` - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of LoDResetGrad opreator should not be null."); - ``` diff --git a/docs/dev_guides/api_contributing_guides/new_cpp_op_notes_en.md b/docs/dev_guides/api_contributing_guides/new_cpp_op_notes_en.md deleted file mode 100644 index d2a12943d7a..00000000000 --- a/docs/dev_guides/api_contributing_guides/new_cpp_op_notes_en.md +++ /dev/null @@ -1,183 +0,0 @@ -# Notes on operator development - -## Building logic of Fluid's op -### 1.Building logic of Fluid's op -All Ops in Fluid are derived from `OperatorBase` , and all Ops are stateless. Each Op contains only four variable members: type, inputs, outputs, and attribute. - -The core method of Op is Run. The Run method requires two resources: data resources and computing resources. These two resources are obtained respectively from `Scope` and `Place`. Inside the framework, there is a global `DeviceContextPool`, which is used to record the mapping relationship between `Place` and `DeviceContext`, which means each `Place` has only one `DeviceContext` corresponding to it, and `DeviceContext` stores the computing resources of the current device. For example, for GPU, these resources include `cudnn_handle`, `cublas_handle`, `stream`, and so on. All the internal calculations (data copy and CUDA Kernel, etc.) of Op must be done in `DeviceContext`. - -The Fluid framework is designed to run on a variety of devices and third-party libraries, and some Op implementations may vary on different the devices or third-party libraries. Therefore, Fluid introduced the OpKernel's approach, which means an Op can have multiple OpKernels. Such Ops are derived from `OperatorWithKernel`, and the representative of such Ops is conv, the OpKernels of conv_op are: `GemmConvKernel`, `CUDNNConvOpKernel`, `ConvMKLDNNOpKernel`, and each OpKernel has two data types, double and float. Ops that do not need OpKernel inclue `WhileOp` and so on. - -Operator inheritance diagram: -![op_inheritance_relation_diagram](./op_inheritance_relation_diagram.png) - -For further information, please refer to: [multi_devices](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/multi_devices) , [scope](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/concepts/scope.md) , [Developer's_Guide_to_Paddle_Fluid](https://github.com/PaddlePaddle/FluidDoc/blob/release/1.2/doc/fluid/getstarted/Developer's_Guide_to_Paddle_Fluid.md) - -### 2.Op's registration logic -The registration entries for each Operator include: - ```C++ - OpCreator creator_; - GradOpMakerFN grad_op_maker_; - proto::OpProto* proto_{nullptr}; - OpAttrChecker* checker_{nullptr}; - InferVarTypeFN infer_var_type_; - InferShapeFN infer_shape_; - ``` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Registration EntryTypeDescriptionUsage
proto::OpProto Class Store the input/output/properties/Op type of Op Call at compile time
GradOpMakerFN Functor Return a set of OpDescs of the reverse Op corresponding to the current Op, because the reverse ones of the forward Op may consist of multiple Ops Call at compile time
OpAttrChecker Class Check the Op's attr Call at compile time
InferVarTypeFN Functor Used to infer the type of the output Var, such as LoDTensor, SelectedRows, or others Call at compile time
InferShapeFN Functor Used to infer the Shape of the Output The usage is different at compile time and runtime. At compile time, it is called in Python side; If the Op is derived from OperatorWithKernel, at the runtime it will be called at op.run
OpCreator Functor Create a new OperatorBase for each call Call at runtime
- -Usually you need to call REGISTER_OPERATOR when you make comments on Op, which is: - ``` - REGISTER_OPERATOR(op_type, - OperatorBase - Op_maker_and_checker_maker, - Op_grad_opmaker, - Op_infer_var_shape, - Op_infer_var_type) - ``` - -**Note:** - -1. For all Op, the first three parameters are required, op_type specifies the name of op, OperatorBase is the object instance of this Op, op_maker_and_checker_maker is the maker of op and the checker of attr in op. -2. If the Op has a reverse, it must have op_grad_opmaker, because in backward, the reverse Op's Maker will be obtained from the forward Op. -3. The framework provides a default op_grad_opmaker:`DefaultGradOpDescMaker`, which will use the input and output of the forward Op as the input of the reverse Op, and the gradients of the input to forward Op's as the output of the reverse Op, and copy the attributes of the forward Op to it. **Note:** DefaultGradOpDescMaker will take all the input and output of the forward Op as the reverse Op input. Even if this input is not necessary, the absence of this will prevent us from doing memory optimization for the unused variables. -4. The framework does not provide a default op_infer_var_shape method. If the Op has no OpKernel, you usually need to add the corresponding op_infer_var_shape method. If the Op has OpKernel, you need to implement the `InferShape` method of `OperatorWithKernel`. You don't need to provide the op_infer_var_shape method. For details, refer to [while_op.cc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/controlflow/while_op.cc), [conv_op.cc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/conv_op.cc). -5. The framework does not provide a default op_infer_var_type method, the user needs to add op_infer_var_type according to the actual situation. Strictly speaking, every Op should register an InferVarType, and op_infer_var_type infers the type and dtype of the output Var according to the type and dtype of the input Var. **Note:** In the Python-side LayerHelper, the create_variable_for_type_inference operation returns a Variable which is a LoDTensor. The C++-side InferVarType can modify the type and dtype of the `Variable`. - - -For more details, please refer to: [How to write a new Op](new_op_en.html) - -## Notes on Writing an Op -### 1. input and output types supported by Op -The input and output of Fluid's Ops are `Variable`. In design, `Variable` can store any type. Op's input and output `Variable` may be of any type, and usually the `Variable` stores `LoDTensor` and `SelectedRows` . - -**Note:** - -- `context.Input("Input")` often appears in the code. It does not mean that the `Variable` of "Input" is `Tensor`, but indicates that the `Tensor` is obtained from `LoDTensor` in the `Variable` of the "Input". If the `Variable` of "Input" is `SelectedRows`, an error will be reported. -- If "Input" is `SelectedRows`, `context->GetInputDim("Input")` will return `var->Get().GetCompleteDims()` instead of Dim of `Tensor` in `SelectedRows` . - -### 2. Do not modify the input data inside Op. -Never make any modification of the input data inside Op, as there may be other Ops that need to read this input. - -### 3. The data type needs to be registered for OpKernel -Currently all OpKernel are required to register double and float data types. - -### 4.Op compatibility issue -The modification of Op needs to consider the compatibility problem. Please ensure that the previous model can be loaded and run normally after the modification of Op which means that the model trained by the old version can be loaded and run with Paddle inference library of new version. **So developers should ensure that the Input, Output and Attribute of OPs cannot be modified (except for documents) or deleted. And developers can add Input, Output and Attribute, but the added Input and Output must be set to be dispensable, and the default value of added Attribute must be set. For more details, please refer to [OP Input/Output/Attribute Compatibility Modification](https://github.com/PaddlePaddle/Paddle/wiki/OP-Input-Output-Attribute-Compatibility-Modification(English-Version))**. - -### 5.Call ShareDataWith -The function of ShareDataWith is to make the two Tensors share the underlying buffer. When calling this operation, special attention should be paid. In the Op, the ShareDataWith cannot be applied to the output of Op. In other words, the Tensor of the Op output must be from Malloc. - -### 6. Sparse gradient parameter's update method -At present, the sparse gradient will first merge the gradient when updating, which is to add up the gradients of the same parameter, and then update the parameters and additional parameters (such as velocity). - -### 7. (Video) Memory optimization -If the reverse of Op does not require all of the input and output of the forward op as its input, please do not use `DefaultGradOpDescMaker`, which will prevent Memory/Video Memory optimization for unused variables. - -### 8. Calls made on Hybrid device -Since the GPU is executed asynchronously, the GPU side may not be actually executed after the CPU call returns. Therefore, if you create a temporary variable in Op that you need to use at the GPU runtime, when the GPU starts running, the temporary variable may have been released on the CPU side, which may cause GPU calculation errors. - -Some of the synchronous and asynchronous operations in the GPU: -``` -The following device operations are asynchronous with respect to the host: - Kernel launches; - Memory copies within a single device's memory; - Memory copies from host to device of a memory block of 64 KB or less; - Memory copies performed by functions that are suffixed with Async; - Memory set function calls. -``` - -Note on cudaMemCpy and cudaMemCpyAsync: - -- If the data transfer is from the GPU side to the CPU side with non-pinned memory , the data transfer will be synchronous, even if an asynchronous copy operation is called. -- If the data is transferred from the CPU side to the CPU side, the data transfer will be synchronous, even if an asynchronous copy operation is called. - -For more information, please refer to: [Asynchronous Concurrent Execution](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#asynchronous-concurrent-execution) , [API synchronization behavior](https://Docs.nvidia.com/cuda/cuda-runtime-api/api-sync-behavior.html#api-sync-behavior) - -## Op Performance Optimization -### 1. Selection of third-party libraries -In the process of writing Op, the operations provided by high-performance libraries (such as cudnn, mkldnn, mklml, eigen, etc.) are preferred, but the benchmark must be done. Some operations in the library may be slower in deep learning tasks. Because the operations provided in high-performance libraries (such as eigen, etc.) are more generalized and in terms of performance, they may not be sufficient. Usually the amount of data in the deep learning model is small, so in some cases some of the high-performance libraries may be compromised to a slower speed. For example, all Op (forward and reverse) of the Elementwise set. The Elementwise operation is called relatively frequently in the model. Especially Elementwise_add, which is used to add offset to many operations. In the previous implementation, Elementwise_op directly calls the Eigen library. Since the Elementwise operation needs to broadcast the data in many cases, and the experiment finds that the Eigen library is slower to broadcast, whose reason is in this PR[#6229](https://github.com/PaddlePaddle/Paddle/pull/6229). - -### 2.Op performance optimization -The calculation speed of Op is related to the amount of data input. For some Op, different calculation methods can be selected according to the attribute parameters in Op and Shape of the input data. For example, concat_op, when axis>=1, in the process of concatenating multiple tensors, you need to make many copies for each tensor. If it is on GPU, you need to call cudaMemCopy. Relative to the CPU, the GPU is an external device. So each time the GPU is called, there will a certain overhead. And when more times of copying are required, the overhead is more prominent. At present, the implementation of concat_op will select different calling methods according to the Shape and axis values of the input data. If there are a relatively large number of input tensors, and the axis is not equal to 0, the multiple copy operations will be converted into a CUDA Kernel to complete the process; if input tensor are less, and the axis is equal to 0, direct copy will be used. The relevant experiment is described in this PR ([#8669](https://github.com/PaddlePaddle/Paddle/pull/8669)) . - -Since the call of CUDA Kernel has a certain overhead, multiple calls of the CUDA Kernel in Op may affect the execution speed of Op. For example, the previous sequence_expand_op contains many CUDA Kernels. Usually, these CUDA Kernels process a small amount of data, so frequent calls to such Kernels will affect the calculation speed of Op. In this case, it is better to combine these small CUDA Kernels into one. This idea is used in the optimization of the sequence_expand_op procedure (related PR[#9289](https://github.com/PaddlePaddle/Paddle/pull/9289)). The optimized sequence_expand_op is about twice as fast as the previous implementation, the relevant experiments are introduced in the PR ([#9289](https://github.com/PaddlePaddle/Paddle/pull/9289)). - -Reduce the number of copy and sync operations between the CPU and the GPU. For example, the fetch operation will update the model parameters and get a loss after each iteration, and the copy of the data from the GPU to the Non-Pinned-Memory CPU is synchronous, so frequent fetching for multiple parameters will reduce the model training speed. - -## Op numerical stability -### 1. Some Ops have numerical stability problems -The main reason for numerical stability is that when the program is run multiple times, the order in which the floating-point data is processed may be different, resulting in different final calculation results. The GPU is accelerated by multi-threaded parallel computing, so it is commonplace that the order of operations on floating-point numbers is not fixed. - -At present, it is found that the result of the convolution operation in cudnn, MaxPooling in cudnn, CudaAtomicXX in CUDA, and aggregation of parameter gradients in Reduce mode of ParallelExecutor are not certain. - -For this purpose, some FLAGS is added to the Fluid. For example, FLAGS_cudnn_deterministic is used to force cudnn to use the deterministic algorithm, and FLAGS_cpu_deterministic to force the CPU-side calculation to use the deterministic method. - -## Other -### 1. Error message -The Enforce prompt message cannot be empty and needs to be written, because the error message can analyze the cause of the error more quickly and conveniently. - -### 2.Op's mathematical formula -If Op has a mathematical formula, be sure to write the mathematical formula in the code and display it in the Doc of the Python API, because the user may need to understand how Paddle implements Op when comparing the calculation results among different frameworks. - -**Note:** The formula preview must be done before the merge to the develop branch. Example: [dynamic_lstmp](../../../api/layers/nn.html#dynamic-lstmp). - -### 3. The order of parameters in the Python-side Op interface -The order of the parameters in the Python API is generally ranked by importance, taking fc as an example: -``` -def fc(input, - size, - num_flatten_dims=1, - param_attr=None, - bias_attr=None, - act=None, - is_test=False, - name=None) -``` diff --git a/docs/dev_guides/git_guides/index_cn.rst b/docs/dev_guides/git_guides/index_cn.rst deleted file mode 100644 index da317adcdc1..00000000000 --- a/docs/dev_guides/git_guides/index_cn.rst +++ /dev/null @@ -1,21 +0,0 @@ -############ -Git 操作指南 -############ - -以下将简单介绍 Git 操作指南以及 Paddle CI 手册，以帮助你快速参与到飞桨的开发中。 - -- `本地开发指南 <./local_dev_guide_cn.html>`_ : 如何在本地进行 Paddle 开发。 -- `代码风格检查指南 <./codestyle_check_guide_cn.html>`_ : 如何在本地进行代码风格检查。 -- `提交 PR 注意事项 <./submit_pr_guide_cn.html>`_ : 提交 PR 相关的注意事项。 -- `Code Review 约定 <./code_review_cn.html>`_ : 飞桨框架对于 Code Review 的一些约定介绍。 -- `Paddle CI 手册 <./paddle_ci_manual_cn.html>`_ : Paddle CI 介绍说明。 - - -.. toctree:: - :hidden: - - local_dev_guide_cn.md - codestyle_check_guide_cn.md - submit_pr_guide_cn.md - code_review_cn.md - paddle_ci_manual_cn.md diff --git a/docs/dev_guides/git_guides/local_dev_guide_en.md b/docs/dev_guides/git_guides/local_dev_guide_en.md deleted file mode 100644 index 84f7e30c397..00000000000 --- a/docs/dev_guides/git_guides/local_dev_guide_en.md +++ /dev/null @@ -1,168 +0,0 @@ -# Guide of local development - -You will learn how to develop programs in local environment under the guidelines of this document. - -## Requirements of coding -- Please refer to the coding comment format of [Doxygen](http://www.doxygen.nl/) -- Unit test is needed for all codes. -- Pass through all unit tests. -- Please follow [regulations of submitting codes](./code_review_cn.html). - -The following guidiance tells you how to submit code. -## [Fork](https://help.github.com/articles/fork-a-repo/) - -Transfer to the home page of GitHub [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) ,and then click button `Fork` to generate the git under your own file directory,such as 。 - -## Clone - -Clone remote git to local: - -```bash -➜ git clone https://github.com/USERNAME/Paddle -➜ cd Paddle -``` - - -## Create local branch - -At present [Git stream branch model](http://nvie.com/posts/a-successful-git-branching-model/) is applied to Paddle to undergo task of development,test,release and maintenance.Please refer to [branch regulation of Paddle](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/others/releasing_process.md) about details。 - -All development tasks of feature and bug fix should be finished in a new branch which is extended from `develop` branch. - -Create and switch to a new branch with command `git checkout -b`. - - -```bash -➜ git checkout -b my-cool-stuff -``` - -It is worth noting that before the checkout, you need to keep the current branch directory clean, otherwise the untracked file will be brought to the new branch, which can be viewed by `git status` . - - -## Use `pre-commit` hook - -Paddle developers use the [pre-commit](http://pre-commit.com/) tool to manage Git pre-commit hooks. It helps us format the source code (C++, Python) and automatically check some basic things before committing (such as having only one EOL per file, not adding large files in Git, etc.). - -The `pre-commit` test is part of the unit test in CI. A PR that does not satisfy the hook cannot be submitted to Paddle. The pre-commit used by Paddle is version 1.10.4. Install `pre-commit` first and then run it in current directory： - - -```bash -➜ pip install pre-commit==1.10.4 -➜ pre-commit install -``` - -Paddle modify the format of C/C++ source code with `clang-format` .Make sure the version of `clang-format` is above 3.8. - -Note：There are differences between the installation of `yapf` with `pip install pre-commit` and that with `conda install -c conda-forge pre-commit` . Paddle developers use `pip install pre-commit` 。 - -## Start development - -I delete a line of README.md and create a new file in the case. - -View the current state via `git status` , which will prompt some changes to the current directory, and you can also view the file's specific changes via `git diff` . - - -```bash -➜ git status -On branch test -Changes not staged for commit: - (use "git add ..." to update what will be committed) - (use "git checkout -- ..." to discard changes in working directory) - modified: README.md -Untracked files: - (use "git add ..." to include in what will be committed) - test -no changes added to commit (use "git add" and/or "git commit -a") -``` - -## Build - -Please refer to [Compile From Source Code](../../../install/compile/fromsource_en.html) about more information of building PaddlePaddle source codes. - -## Test - -Any new unit testing file of the format `test_*.py` added to the directory `test/` is automatically added to the project to compile. - -Remarks: - -- **running unit tests requires compiling the entire project** and requires compiling with flag `WITH_TESTING` on i.e. `cmake paddle_dir -DWITH_TESTING=ON`. - -- **To execute a single test, you must use the ctest command**, not use `python test_*.py` - -After successfully compiling the project, run the following command to run unit tests: - -```bash -ctest -R test_mul_op -V -``` - -## Commit - -Next we cancel the modification of README.md,and submit new added test file. - -```bash -➜ git checkout -- README.md -➜ git status -On branch test -Untracked files: - (use "git add ..." to include in what will be committed) - test -nothing added to commit but untracked files present (use "git add" to track) -➜ git add test -``` - -It's required that the commit message is also given on every Git commit, through which other developers will be notified of what changes have been made. Type `git commit` to realize it. - -```bash -➜ git commit -CRLF end-lines remover...............................(no files to check)Skipped -yapf.................................................(no files to check)Skipped -Check for added large files..............................................Passed -Check for merge conflicts................................................Passed -Check for broken symlinks................................................Passed -Detect Private Key...................................(no files to check)Skipped -Fix End of Files.....................................(no files to check)Skipped -clang-formater.......................................(no files to check)Skipped -[my-cool-stuff c703c041] add test file - 1 file changed, 0 insertions(+), 0 deletions(-) - create mode 100644 233 -``` - - -## Keep the latest local repository - -It needs to keep up with the latest code of original repository(）before Pull Request. - -Check the name of current remote repository with `git remote`. - -```bash -➜ git remote -origin -➜ git remote -v -origin https://github.com/USERNAME/Paddle (fetch) -origin https://github.com/USERNAME/Paddle (push) -``` - -origin is the name of remote repository that we clone,which is also the Paddle under your own account. Next we create a remote host of an original Paddle and name it upstream. - -```bash -➜ git remote add upstream https://github.com/PaddlePaddle/Paddle -➜ git remote -origin -upstream -``` - -Get the latest code of upstream and update current branch. - -```bash -➜ git fetch upstream -➜ git pull upstream develop -``` - -## Push to remote repository - -Push local modification to GitHub(https://github.com/USERNAME/Paddle). - -```bash -# submit it to remote git the branch my-cool-stuff of origin -➜ git push origin my-cool-stuff -``` diff --git a/docs/dev_guides/git_guides/submit_pr_guide_cn.md b/docs/dev_guides/git_guides/submit_pr_guide_cn.md deleted file mode 100644 index 05ae3de9c47..00000000000 --- a/docs/dev_guides/git_guides/submit_pr_guide_cn.md +++ /dev/null @@ -1,90 +0,0 @@ -# 提交 PR 注意事项 - -## 提交 Pull Request - -- 请注意 commit 的数量： - -原因：如果仅仅修改一个文件但提交了十几个 commit，每个 commit 只做了少量的修改，这会给评审人带来很大困扰。评审人需要逐一查看每个 commit 才能知道做了哪些修改，且不排除 commit 之间的修改存在相互覆盖的情况。 - -建议：每次提交时，保持尽量少的 commit。可以通过`git rebase -i HEAD~3`将最新的 3 个 commit 合并成一个（你可以根据实际情况修改该数值），再 Push 到远程仓库，可以参考[rebase 用法](https://www.jianshu.com/p/4a8f4af4e803)。 - -- 请注意每个 commit 的名称：应能反映当前 commit 的内容，不能太随意。 - -- 请不要频繁 Merge develop 分支（在过 CI 时，会自动 Merge develop），这样会使 CI 重跑，更加延长 CI 通过时间。 - -- 评审人 review 过后，不允许使用 git push -f 强行提交代码，这样评审人无法看到修改前后的 diff，使评审变得困难。 - -## 完成 Pull Request PR 创建 - -切换到所建分支，然后点击 `Compare & pull request`。 - -![new_pull_request](../images/compare_pull_request.png) - -选择目标分支： - -![change_base](../images/change_base.png) - -如果解决了某个 Issue 的问题，请在该 PUll Request 的第一个评论框中加上：fix #issue_number，这样当该 PUll Request 被合并后，会自动关闭对应的 Issue。关键词包括：close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved，请选择合适的词汇。详细可参考[Closing issues via commit messages](https://help.github.com/articles/closing-issues-via-commit-messages/) - -接下来等待 review，如果有需要修改的地方，参照上述步骤更新 origin 中的对应分支即可。 - -## 签署 CLA 协议和通过单元测试 - -### 签署 CLA - -在首次向 PaddlePaddle 提交 Pull Request 时，你需要签署一次 CLA(Contributor License Agreement)协议，以保证你的代码可以被合入，具体签署方式如下： - -- 请你查看 PR 中的 Check 部分，找到 license/cla，并点击右侧 detail，进入 CLA 网站 - -

- - - -
- -- 请你点击 CLA 网站中的“Sign in with GitHub to agree”,点击完成后将会跳转回你的 Pull Request 页面 - -
- - - -
- - -### 通过单元测试 - -你在 Pull Request 中每提交一次新的 commit 后，会触发 CI 单元测试，请确认你的 commit message 中已加入必要的说明，请见[提交（commit）](./local_dev_guide_cn.html#commit) - -请你关注你 Pull Request 中的 CI 单元测试进程，它将会在几个小时内完成 - -当所需的测试后都出现了绿色的对勾，表示你本次 commit 通过了各项单元测试，你只需要关注显示 Required 任务，不显示的可能是我们正在测试的任务 - -如果所需的测试后出现了红色叉号，代表你本次的 commit 未通过某项单元测试，在这种情况下，请你点击 detail 查看报错详情，优先自行解决报错问题，无法解决的情况，以评论的方式添加到评论区中，我们的工作人员将和你一起查看 - - -## 删除远程分支 - -在 PR 被 merge 进主仓库后，我们可以在 PR 的页面删除远程仓库的分支。 - -![delete_branch](../../guides/10_contribution/img/delete_branch.png) - -也可以使用 `git push origin :分支名` 删除远程分支，如： - -```bash -➜ git push origin :my-cool-stuff -``` - - -## 删除本地分支 - -最后，删除本地分支。 - -```bash -# 切换到 develop 分支 -➜ git checkout develop - -# 删除 my-cool-stuff 分支 -➜ git branch -D my-cool-stuff -``` - -至此，我们就完成了一次代码贡献的过程。 diff --git a/docs/dev_guides/git_guides/submit_pr_guide_en.md b/docs/dev_guides/git_guides/submit_pr_guide_en.md deleted file mode 100644 index 22f90a2c0d9..00000000000 --- a/docs/dev_guides/git_guides/submit_pr_guide_en.md +++ /dev/null @@ -1,91 +0,0 @@ -# Guide of submitting PR to GitHub - -## Tthe submit of Pull Request - -- Please note the number of commit: - -Reason：It will bother reviewers a lot if a dozen of commits are submitted after modification of only one file and only a few modifications are updated in every commit.Reviewers have to check commit one by one to figure out the modification.And sometimes it needs to take the overlap among commits into consideration. - -Suggestion: Keep as few commits as possible for each commit. You can merge the latest 3 commits into one through `git rebase -i HEAD~3` (you can modify the value according to the actual situation).you can refer to [rebase usage](https://www.jianshu.com/p/4a8f4af4e803). - -- Pay attention to the name of every commit:It would be better to abstract the content of present commit and be not too arbitrary. - -- Please do not merge the develop branch frequently (when passing CI, it will automatically merge develop), which will make the CI run again and prolong the CI passing time. - -- After the reviewer has reviewed, it is not allowed to use git push -f to forcibly submit the code, so that the reviewer cannot see the diff before and after the modification, making the review difficult. - -## Finish Pull Request create PR - -Create an Issue to describe your problem and keep its number. - -Switch to the branch you have created and click `Compare & pull request`。 - -![new_pull_request](../images/compare_pull_request.png) - -Switch to targeted branch: - -![change_base](../images/change_base.png) - -If the issue of an Issue is resolved, please add: fix #issue_number to the first comment box of the PUll Request, so that when the PUll Request is merged, the corresponding Issue will be automatically closed. Keywords include: close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved, please choose the appropriate word. For details, please refer to - -Then please wait for review.If there is any need to make a modification,you can update corresponding branch in origin following the steps above. - - -## Sign CLA and pass unit tests - -### Sign CLA - -For the first time to submit Pull Request,you need to sign CLA(Contributor License Agreement) to ensure merge of your code.Specific steps are listed as follows: - -- Please check the Check in PR to find license/cla and click detail on the right to change into CLA website. - -
- - - -
- -- Please click “Sign in with GitHub to agree” in CLA website.It will change into your Pull Request page after the click. - -
- - - -
- - -### Pass unit tests - -Every new commit in your Pull Request will trigger CI unit tests,so please make sure that necessary comments have been included in your commit message.Please refer to [commit](./local_dev_guide_cn.html#commit) - -Please note the procedure of CI unit tests in your Pull Request which will be finished in several hours. - -Green ticks after all tests means that your commit has passed all unit tests,you only need to focus on showing the Required tasks, the ones not showing may be the tasks we are testing. - -Red cross after the tests means your commit hasn't passed certain unit test.Please click detail to view bug details and make a screenshot of bug,then add it as a comment in your Pull Request.Our stuff will help you check it. - - -## Delete remote branch - -We can delete branches of remote repository in PR page after your PR is successfully merged into master repository. - -![delete_branch](../guides/10_contribution/img/delete_branch.png) - -We can also delete the branch of remote repository with `git push origin :the_branch_name`,such as: - -```bash -➜ git push origin :my-cool-stuff -``` - -## Delete local branch - -Finally,we delete local branch - -```bash -# Switch to develop branch -➜ git checkout develop -# delete my-cool-stuff branch -➜ git branch -D my-cool-stuff -``` - -And now we finish a full process of code contribution diff --git a/docs/eval/evaluation_of_docs_system.md b/docs/eval/evaluation_of_docs_system.md deleted file mode 100644 index f446855348c..00000000000 --- a/docs/eval/evaluation_of_docs_system.md +++ /dev/null @@ -1,851 +0,0 @@ -# 飞桨官网文档体验报告 - -## 上下文介绍 - -### 文档体系的分类 - -按照 https://documentation.divio.com/ 中的理论，任何软件产品，都应该配套以下四类文档： - -![](./images/documentation-system.png) - -本文将基于这个文档分类系统，参考比较多个深度学习框架的文档组织方式，形成最终报告。 - -### 竞品比较 - -#### TensorFlow - - - Tutorial：[Tutorial](https://www.tensorflow.org/tutorials) - - GUIDE：[Guide](https://www.tensorflow.org/guide) - - EXPLANATION：除 [ML Glossary](https://developers.google.com/machine-learning/glossary/tensorflow) 外，没有显式存在。 - - REFERENCE： [API 文档](https://www.tensorflow.org/versions) - -TensorFlow 的文档规划，比较直接地匹配了本文所介绍的分类标准。 - -具体的文档结构如下： - -**Tutorials** - -- Quickstart for beginners -- Quickstart for experts -- ML basics with Keras - - Basic image classfication - - Basic text classification - - Text classification with TF Hub - - Regression - - Overfit and underfit - - Save and load - - Tune hyperprameters with Keras Tuner - - More examples on kera.io -- Load and preprocess data - - Images - - CSV - - Numpy - - pandas.DataFrame - - TFRecord and tf.Example - - Additional formats with tf.io - - Text - - More text loading -- Customization - - Tensors and operations - - Custom layers - - Custom traning: walkthrough -- Distributed training - - Distributed training with Kereas - - Distributed training with DTensors - - Using DTensors with Keras - - Custom training loops - - Multi-worker training with Keras - - Multi-worker training with CTL - - Parameter Server Training - - Save and load - - Distributed input -- Images - - Convolutional Neural Network - - Image classification - - Transfer learning and fine-tuning - - Transfer learning with TF Hub - - Data Augmentaion - - Image segmentation - - Object detection with TF Hub -- Text - - Word embeddings - - Word2Vec - - Text classification with an RNN - - classify Text with BERT - - Solve GLUE tasks using BERT on TPU - - Neural machine translation with attention - - Image captioning -- Audio - - Simple audio recognition - - Transfer learning for audio recognition - - Generate music with an RNN -- Structured data - - Classifying structured data with preprocessing layers - - Classfication on imbalanced data - - Time series forecasting - - Decision forest models - - Recommenders -- Generative - - Neural style transfer - - DeepDream - - DCGAN - - Pix2Pix - - CycleGAN - - Adversarial FGSM - - Intro to Autoencoders - - Variational Autoencoder -- Model Understanding - - Integrated gradients - - Uncertainty quantification with SNGP - - Probabilistic regression -- Reinforcement learning - - Actor-Critic method - - TensorFlow agents -- tf.Estimator - - Premade estimator - - Linear model - - Keras model to Estimator - - Multi-worker training with Estimator - - Feature columns - -**Guide** - -- TensorFlow basics - - Overview - - Tensors - - Variables - - Automatic differentiation - - Graphs and functions - - Modules, layers, and models - - Training loops -- Keras - - The Sequnetial model - - The Functional API - - Training and evaluation with the built-in methods - - Making new Laayers and Models via subclassing - - Save and load Kears models - - Working with preprocessing layers - - Customize what happens in Model.fit - - Writing a training loop from scratch - - Recurrent Neural Networks(RNN) with Keras - - Masking and padding wiht Keras - - Writing your own callbacks - - Transfer learning and fine-tuning - - Training Keras models with TensorFlow Cloud -- TensorFlow in depth - - Tensor slicing - - Advanced autodiff - - Ragged tensor - - Sparse tensor - - Random number generation - - NumPy API - - DTensor concepts - - Thinking in TensorFlow 2 -- Customization - - Create an op - - Extension types -- Data input pipelines - - tf.data - - Optimize pipeline performance - - Analyze pipeline performance -- Save a model - - Checkpoint - - SavedModel -- Accelerators - - Distributed training - - GPU - - TPU -- Performance - - Better performance with tf.function - - Profile TensorFlow performance - - Optimize GPU Performance - - Graph optimizaition - - Mixed precision - - -#### PyTorch - - - Tutorial：https://pytorch.org/tutorials/beginner/basics/intro.html - - GUIDE：https://pytorch.org/tutorials/recipes/recipes_index.html - - EXPLANATION：没有显式存在，分散在 API 文档中。 - - REFERENCE：[API 文档](https://pytorch.org/docs/stable/index.html) - -[PyTorch 的文档](https://docs.pytorch.org) 分为 API 文档和 Tutorials 两大类。但实际 Tutorials 中可以继续分为 Learn the Basics 和 PyTorch Recipes 两大类。 - -并且，PyTorch 在自己的 API 文档首页中有 Notes。在必要时，也会在模块 API 开始做背景介绍，这些内容可以归为 “Explanation” 象限。 - -- Introduction to PyTorch - - Learn the Basics - - Quickstart - - Tensors - - Datasets & Dataloaders - - Transforms - - Build the Neural Network - - Automatic Differentiation with torch.autograd - - Optimization Model Parameters - - Save and Load the Model -- Introduction to PyTorch on YouTube - - Introduction to PyTorch - YouTube Series - - Introduction to PyTorch - - Introduction to PyTorch Tensors - - The Fundamentals of Autograd - - Building Models with PyTorch - - PyTorch TensorBoard Support - - Traning wiht PyTorch - - Model Understanding with Captum -- Learning PyTorch - - Deep Learning with PyTorch: A 60 Minute Blitz - - Learing PyTorch with Examples - - What is torch.nn really? - - Visualizing Models, Data, and Training with TensorBoard -- Image and Video - - TorchVision Object Detection Finetuning Tutorial - - Transfer Learning for Computer Vision Tutorial - - Adversarial Example Generation - - DCGAN Tutorial - - Spatial Tansformer Networks Tutorial - - Optimizaing Vision Transformer Model for Deployment -- Audio - - Audio I/O - - Audio Resampling - - Audio Data Augmentation - - Audio Feature Extractions - - Audio Feature Augmentation - - Audio Datasets - - Speech Recognition with Wav2Vec2 - - Speech Command Classification with torchaudio - - Text-to-speech with torchaudio - - Forced Alignment with Wav2Vec2 -- Text - - Language Modeling with nn.Transformer and TorchText - - NLP From Scratch: Classifying Names with a Character-Level RNN - - NLP From Scratch: Generating Names with a Character-Level RNN - - NLP From Scratch: Translation with a Sequence to Sequence Network and Attention - - Text classification with the torchtext library Language Translation with nn.Transformer and torchtext -- Deploying PyTorch Models in Production - - Deploying PyTorch in Python via a REST API with Flask - - Introduction to TorchScript - - Loading a TorchScript Model in C++ - - (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime - - Real Time Inference on Raspberry Pi 4 -- Code Transforms with FX - - Building a Convolution/Batch Norm fuser in FX - - Building a Simple CPU Performance Profiler with FX -- Frontend APIs - - Channels Last Memory Format in PyTorch - - Forward-mode Automatic Differentiation - - Using the PyTorch C++ Frontend - - Dynamic Parallelism in TorchScript - - Autograd in C++ Frontend -- Extending PyTorch - - Double Backward with Custom Functions - - Fusing Convolution and Batch Norm using Custom Function - - Custom C++ and CUDA Extensions - - Extending TorchScript with Custom C++ Operators - - Extending TorchScript with Custom C++ Classes - - Registering a Dispatched Operator in C++ - - Extending dispatcher for a new backend in C++ -- Model Optimization - - Profiling your PyTorch Module - - PyTorch Profiler with TensorBoard - - Hyperparameter tuning with Ray Tune - - Optimizing Vision Transformer Model for Deployment Parametrization Tutorial - - Pruning Tutorial - - Dynamic Quantization on an LSTM Word Language Model - - Dynamic Quantization on BERT - - Quantized Transfer Learning for Computer Vision Tutorial - - Static Quantization with Eager Model in PyTorch - - Grokking PyTorch Intel CPU performance from first principles -- Parallel and Distributed Tranining - - PyTorch Distributed Overview - - Single-Machine Model Parallel Best Practices - - Getting Started with Distributed Data Parallel - - Writing Distributed Applications with PyTorch - - Getting Started with Fully Sharded Data Prallel - - Customize Process Group Backends Using Cpp Extension - - Getting Started with Distributed RPC Framework - - Implementing a Parameter Server Using Distributed RPC Framework - - Distributed Pipeline Parallelsim using RPC - - Implementing Batch RPC Processing Using Asynchronous Executions - - Combining Distributed DataPrallel with Distributed RPC Framework - - Training Transformer models using Pipeline Parallelism - - Training Transformer models using Distributed Data Parallel and Pipeline Parallelism - - Distributed Training with Uneven Inputs Using the Join Context Manager -- Moible - - Image Segmentation DeepLabV3 on iOS - - Image Segmentation DeepLabV3 on Android -- Recommendation Systems - - Introduction to TorchRec - - - -#### MindSpore - - - Tutorial：在 [教程](https://www.mindspore.cn/tutorials/zh-CN/r1.6/index.html) 前部分和 [编程指南](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/index.html) 的前半部分话题中 - - GUIDE：在 [教程](https://www.mindspore.cn/tutorials/zh-CN/r1.6/index.html) 后部分和 [编程指南](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/index.html) 的后半段话题以及 [开发者精华分享](https://bbs.huaweicloud.com/forum/forumdisplay-fid-1076-orderby-lastpost-filter-typeid-typeid-1255sub.html) 中 - - EXPLANATION：[规格和说明](https://www.mindspore.cn/docs/note/zh-CN/r1.6/index.html) 及 [FAQ](https://www.mindspore.cn/docs/faq/zh-CN/r1.6/index.html) - - REFERENCE：[API 文档](https://www.mindspore.cn/docs/api/zh-CN/r1.6/index.htmls) - -MindSpore 的有自己独立的文档分类标准和风格，所以硬套本文提及的文档分类标准，结果会显得有些复杂。以上所列的各类文档中，《开发者精华分享》是比较独特的一个栏目，他更像是 MindSpore 搭建的开源社区平台，吸收了用户贡献的各种经验，包括 Numpy 的使用，MindSpore 的安装问题如何解决等社区贡献的知识。 - -**初学入门** - -- 初学教程 - - 基本介绍 - - 快速入门 - - 张量 Tensor - - 数据处理 - - 创建网络 - - 自动微分 - - 模型训练 - - 保存与加载 - - 推理与部署 -- 进阶 - - 进阶案例：线性拟合 - - 数据处理 - - 网络构建 - - 训练与评估 - - 动态图与静态图 - -**应用实践** - -- 计算机视觉 - - ResNet50 网络进行图像分类 - - 图像分类迁移学习 - - 模型对抗攻击 - - 生成式对抗网络 -- 自然语言处理 - - 使用 RNN 实现情感分类 - - LSTM+CRF 实现序列标注 - -**深度开发** - -- 数据处理 - - 自动数据增强 - - 轻量化数据处理 - - 单节点数据缓存 - - 优化数据处理 - -- 算子执行 - - 算子分类 - - 运算重载 - - 自定义算子（CPU） - - 自定义算子（GPU） - - 自定义算子（Ascend） - - 自定义算子（基于 Custom 表达） - -- 模型推理 - - 模型推理总览 - - GPU/CPU 推理 - - Ascend 910 AI 处理器上推理 - - Ascend 310 AI 处理器上使用 MindIR 模型进行推理 - - Ascend 310 AI 处理器上使用 AIR 模型进行推理 - -- 调试调优 - - 查看中间文件 - - Dump 功能调试 - - 自定义调试信息 - - 调用自定义类 - - 算子增量编译 - - 算子调优工具 - - 自动数据加速 - - 固定随机性以复现脚本运行结果 - -- 分布式并行 - 分布式并行总览 - 分布式集合通信原语 - 分布式并行训练基础样例（Ascend） - 分布式并行训练基础样例（GPU） - 分布式推理 - 保存和加载模型（HyBrid Parallel 模式） - 分布式并行训练 Transformer 模型 - 鹏程·盘古模型网络多维度混合并行解析 - 分布式故障恢复 - -- 其他特性 - - 混合精度 - - 梯度累积算法 - - 自适应梯度求和算法 - - 降维训练算法 - -- 设计 - - 设计白皮书 - - 全场景统一 - - 函数式微分编程 - - 动静态图结合 - - 异构并行训练 - - 分布式并行 - - 中间表达 MindIR - - 高性能数据处理引擎 - - 图算融合加速引擎 - - 二阶优化 - - 可视化调试调优 - - 安全可信 - - 术语 - -- 迁移指南 - - 概述 - - 准备工作 - - 网络脚本分析 - - 网络脚本开发 - - 网络调试 - - 精度调试 - - 性能调试 - - 推理执行 - - 网络迁移调试实例 - - 常见问题 - -### 完备性定义 - -综合 Paddle 及其它竞品，先罗列一个较为完备的“知识点体系”。即如果是各个级别的用户，面向他们应该提供哪类信息，做一个全集式的罗列。 - -- 初级用户：初级用户定位是未有使用深度学习框架完成项目的经验，如高校低年级学生 -- 中级用户：中级用户定位是通常意义上的算法工程师，在日常的项目中需要使用深度学习框架，但还不涉及较为“高级”的特性。如独立参与比赛的高校学生，工业界的初级、中级算法工程师。 -- 高级用户：需要紧随前沿使用框架高级特性的工业届、学术界用户。如复杂的并行技术、算子融合、AMP 等高级优化技术。 - -对应的信息分类和罗列： - -- 初级： - - 基本数据（Tensor）和基本算子（Op） - - 数据加载 - - 如何组网 - - 如何训练 - - 保存与加载模型 - - 可视化、调优技巧 - - 自动微分 - -- 中级： - - 动态图与静态图的转换 - - 部署相关 - - CV 领域的实践指南 - - NLP 领域的实践指南 - - 推荐系统领域的实践指南 - - 强化学习领域的实践指南 - - 移动端相关 - - 从其它框架至本框架的迁移 - -- 高级： - - 如何自定义算子 - - 分布式训练 - - 框架设计文档 - -## Paddle 文档体系分析 - -现有 Paddle 文档，结构如下： - -**文档** - -- 整体介绍 - - 基本概念 - - 升级指南 - - 版本迁移工具 -- 模型开发 - - 10 分钟快速上手飞桨 - - 数据集的定义和加载 - - 数据预处理 - - 模型组网 - - 训练与预测验证 - - 单机多卡训练 - - 自定义指标 - - 模型保存与载入 - - 模型导出 ONNX 协议 -- VisualDL 工具 - - VisualDL 工具简介 - - VisualDL 使用指南 -- 动态图转静态图 - - 使用样例 - - 转换原理 - - 支持语法 - - 案例解析 - - 报错调试 -- 推理部署 - - 服务器部署 - - 移动端/嵌入式部署 - - 模型压缩 -- 分布式训练 - - 分布式训练开始 - - 使用 FleetAPI 进行分布式训练 -- 自定义算子 - - 自定义原生算子 - - 原生算子开发注意事项 - - 自定义外部算子 - - 自定义 Python 算子 - - Kernel Primitive API - - API 介绍 - - API 示例 -- 性能调优 - - 飞桨模型量化 -- 算子映射 - - Paddle 1.8 与 Paddle 2.0 API 映射表 - - PyTorch-PaddlePaddle API 映射表 -- 硬件支持 - - 飞桨产品硬件支持表 - - 昆仑 XPU 芯片运行飞桨 - - 海光 DCU 芯片运行飞桨 - - 昇腾 NPU 芯片运行飞桨 -- 参与开发 - - 本地开发指南 - - 提交 PR 注意事项 - - FAQ -- 环境变量 FLAGS - - cudnn - - 数值计算 - - 调试 - - check nan inf 工具 - - 设备管理 - - 分布式 - - 执行器 - - 存储管理 - - 曻腾 NPU - - 其它 - -**应用实践** - -- 快速上手 - - hello paddle：从普通程序走向机器学习程序 - - 动态图 - - 飞桨高层 API 使用指南 - - 模型保存及加载 - - 使用线性回归预测波士顿房价 -- 计算机视觉 - - 使用 LeNet 在 MNIST 数据集实现图像分类 - - 使用卷积神经网络进行图像分类 - - 基于图片相似度的图片搜索 - - 基于 U-Net 卷积神经网络实现宠物图像分割 - - 通过 OCR 实现验证码识别 - - 通过 Sub-Pixel 实现图像超分辨率 - - 人脸关键点检测 - - 点云处理：实现 PointNet 点云分类 -- 自然语言处理 - - 用 N-Gram 模型在莎士比亚文集中训练 word embedding - - IMDB 数据集使用 BOW 网络的文本分类 - - 使用预训练的词向量完成文本分类任务 - - 使用注意力机制的 LSTM 的机器翻译 - - 使用序列到序列模型完成数字加法 -- 推荐 - - 使用协同过滤实现电影推荐 -- 强化学习 - - 强化学习——Actor Critic Method - - 强化学习——Advantage Actor-Critic(A2C) - - 强化学习——Deep Deterministic Policy Gradient (DDPG) -- 时序数据 - - 通过 AutoEncoder 实现时序数据异常检测 -- 动转静 - - 使用动转静完成以图搜图 - - -### 完备性分析 - -根据以上对同类软件的文档做了调研罗列的基础上，对它们的知识点，以及知识点对应的文章，做了初步的统计。 - -有几点说明： - -- TensorFlow、PaddlePaddle 因为已经将部署、移动端推理独立成了产品，所以以上表格中统计数据可能不准确，或未做统计 -- PaddlePaddle 的 [教程](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3465990) 一栏中也包含了大量的文章，但是未做统计（因为不在 `docs` 仓库中），不过会在下文中的“组织结构分析中”纳入考量 - -| | TensorFlow | 数量 | PyTorch | 数量 | MindSpore | 数量 | PaddlePaddle | 数量 | -| ---------------------------- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ------ | -| 基本数据（Tensor）和基本算子 | Tensors Variables Tensor slicing Ragged tensor Sparse tensor DTensor concepts | 6 | Tensors Transforms Introduction to PyTorch Tensors | 3 | 张量 Tensor | 1 | Tensor 概念介绍 | 1 | -| 数据加载与预处理 | Images CSV Numpy pandas.DataFrame TFRecord and tf.Example Additional formats with tf.io Text More text loading Classifying structured data with preprocessing layers Classfication on imbalanced data Time series forecasting Decision forest models | 13 | Datasets & Dataloaders | 1 | 数据处理 数据处理（进阶） 自动数据增强 轻量化数据处理 单节点数据缓存 优化数据处理 | 6 | 数据集的定义和加载 数据预处理 | 2 | -| 如何组网 | Modules, layers, and models | 1 | Build the Neural Network Building Models with PyTorch What is torch.nn really? Learing PyTorch with Examples | 4 | 创建网络 网络构建 | 2 | 模型组网 飞桨高层 API 使用指南 层与模型 | 3 | -| 如何训练 | Training loops NumPy API Checkpoint SavedModel | 4 | Optimization Model Parameters Traning wiht PyTorch | 2 | 模型训练 训练与评估 | 2 | 训练与预测验证 自定义指标 | 2 | -| 保存与加载模型 | Save and load Save and load(Distrbuted Training) | 2 | Save and Load the Model | 1 | 保存与加载 | 1 | 模型保存与载入 模型保存及加载（应用实践） | 2 | -| 可视化、调优技巧 | Overfit and underfit Tune hyperprameters with Keras Tuner Better performance with tf.function Profile TensorFlow performance Graph optimizaition Optimize GPU Performance Mixed precision | 7 | PyTorch TensorBoard Support Model Understanding with Captum Visualizing Models, Data, and Training with TensorBoard Profiling your PyTorch Module PyTorch Profiler with TensorBoard Hyperparameter tuning with Ray Tune Optimizing Vision Transformer Model for Deployment Parametrization Tutorial Pruning Tutorial Grokking PyTorch Intel CPU performance from first principles | 11 | 查看中间文件 Dump 功能调试 自定义调试信息 调用自定义类 算子增量编译 算子调优工具 自动数据加速 固定随机性以复现脚本运行结果 | 8 | VisualDL 工具简介 VisualDL 使用指南 飞桨模型量化 | 3 | -| 自动微分 | Automatic differentiation Advanced autodiff | 2 | Automatic Differentiation with torch.autograd The Fundamentals of Autograd | 2 | 自动微分 | 1 | 自动微分 | 1 | -| 动态图与静态图 | Graphs and functions | 1 | （torchscript 其实是静态图，不过归类到部署中了） | 0 | 动态图与静态图 | 1 | 使用样例 转换原理 支持语法 案例解析 报错调试 动态图 使用动转静完成以图搜图 | 7 | -| 部署相关 | https://www.tensorflow.org/tfx/tutorials 下的 21 篇文章 https://www.tensorflow.org/tfx/guide 下的 30+文章 | 50+ | Deploying PyTorch in Python via a REST API with Flask Introduction to TorchScript Loading a TorchScript Model in C++ (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime Real Time Inference on Raspberry Pi 4 | 6 | 推理与部署 模型推理总览 GPU/CPU 推理 Ascend 910 AI 处理器上推理 Ascend 310 AI 处理器上使用 MindIR 模型进行推理 Ascend 310 AI 处理器上使用 AIR 模型进行推理 | 7 | 服务器部署 移动端/嵌入式部署 模型压缩 https://www.paddlepaddle.org.cn/lite/v2.10/guide/introduction.html 下 50+ 篇文章 | 50+ | -| CV 领域相关 | Basic image classfication Convolutional Neural Network Image classification Transfer learning and fine-tuning Transfer learning with TF Hub Data Augmentaion Image segmentation Object detection with TF Hub Neural style transfer DeepDream DCGAN Pix2Pix CycleGAN Adversarial FGSM Intro to Autoencoders Variational Autoencoder | 16 | TorchVision Object Detection Finetuning Tutorial Transfer Learning for Computer Vision Tutorial Adversarial Example Generation DCGAN Tutorial Spatial Tansformer Networks Tutorial Optimizaing Vision Transformer Model for Deployment Quantized Transfer Learning for Computer Vision Tutorial | 7 | ResNet50 网络进行图像分类 图像分类迁移学习 模型对抗攻击 生成式对抗网络 | 4 | 使用 LeNet 在 MNIST 数据集实现图像分类 使用卷积神经网络进行图像分类 基于图片相似度的图片搜索 基于 U-Net 卷积神经网络实现宠物图像分割 通过 OCR 实现验证码识别 通过 Sub-Pixel 实现图像超分辨率 人脸关键点检测 点云处理：实现 PointNet 点云分类 | 7 | -| NLP 领域相关 | Basic text classification Text classification with TF Hub Word embeddings Word2Vec Text classification with an RNN classify Text with BERT Solve GLUE tasks using BERT on TPU Neural machine translation with attention Image captioning | 9 | Language Modeling with nn.Transformer and TorchText NLP From Scratch: Classifying Names with a Character-Level RNN NLP From Scratch: Generating Names with a Character-Level RNN NLP From Scratch: Translation with a Sequence to Sequence Network and Attention Text classification with the torchtext library Language Translation with nn.Transformer and torchtext Dynamic Quantization on an LSTM Word Language Model Dynamic Quantization on BERT | 8 | 使用 RNN 实现情感分类 LSTM+CRF 实现序列标注 | 2 | 用 N-Gram 模型在莎士比亚文集中训练 word embedding IMDB 数据集使用 BOW 网络的文本分类 使用预训练的词向量完成文本分类任务 使用注意力机制的 LSTM 的机器翻译 使用序列到序列模型完成数字加法 | 5 | -| 语音领域相关 | | | Audio I/O Audio Resampling Audio Data Augmentation Audio Feature Extractions Audio Feature Augmentation Audio Datasets Speech Recognition with Wav2Vec2 Speech Command Classification with torchaudio Text-to-speech with torchaudio Forced Alignment with Wav2Vec2 | 10 | | 0 | | 0 | -| 推荐领域相关 | Recommenders | 1 | Introduction to TorchRec | 1 | | 0 | 使用协同过滤实现电影推荐 | 1 | -| 强化学习领域相关 | Actor-Critic method TensorFlow agents | 2 | 0 | 0 | | 0 | 强化学习——Actor Critic Method 强化学习——Advantage Actor-Critic(A2C) 强化学习——Deep Deterministic Policy Gradient (DDPG) | 3 | -| 移动端相关 | 独立的栏目 https://www.tensorflow.org/lite | 10+ | Image Segmentation DeepLabV3 on iOS Image Segmentation DeepLabV3 on Android | 2 | | 0 | Paddle Lite 中独立存在 | 未统计 | -| 框架之间的迁移相关 | | | | 0 | 概述 准备工作 网络脚本分析 网络脚本开发 网络调试 精度调试 性能调试 推理执行 网络迁移调试实例 常见问题 | 10 | Paddle 1.8 与 Paddle 2.0 API 映射表 PyTorch-PaddlePaddle API 映射表 版本迁移工具 | 3 | -| 自定义算子 | Tensors and operations Custom layers Custom traning: walkthrough Create an op Extension types | 5 | Double Backward with Custom Functions Fusing Convolution and Batch Norm using Custom Function Custom C++ and CUDA Extensions Extending TorchScript with Custom C++ Operators Extending TorchScript with Custom C++ Classes Registering a Dispatched Operator in C++ Extending dispatcher for a new backend in C++ | 7 | 算子分类 运算重载 自定义算子（CPU） 自定义算子（GPU） 自定义算子（Ascend） 自定义算子（基于 Custom 表达） | 6 | 自定义原生算子 原生算子开发注意事项 自定义外部算子 自定义 Python 算子 API 介绍 API 示例 本地开发指南 提交 PR 注意事项 FAQ | 9 | -| 分布式训练 | Distributed training with Kereas Distributed training with DTensors Using DTensors with Keras Custom training loops Multi-worker training with Keras Multi-worker training with CTL Parameter Server Training Distributed input Distributed training | 9 | PyTorch Distributed Overview Single-Machine Model Parallel Best PracticesGetting Started with Distributed Data Parallel Writing Distributed Applications with PyTorch Getting Started with Fully Sharded Data Prallel Customize Process Group Backends Using Cpp Extension Getting Started with Distributed RPC Framework Implementing a Parameter Server Using Distributed RPC Framework Distributed Pipeline Parallelsim using RPC Implementing Batch RPC Processing Using Asynchronous Executions Combining Distributed DataPrallel with Distributed RPC Framework Training Transformer models using Pipeline Parallelism Training Transformer models using Distributed Data Parallel and Pipeline Parallelism Distributed Training with Uneven Inputs Using the Join Context Manager | 16 | 分布式并行总览 分布式集合通信原语 分布式并行训练基础样例（Ascend） 分布式并行训练基础样例（GPU） 分布式推理 保存和加载模型（HyBrid Parallel 模式） 分布式并行训练 Transformer 模型 鹏程·盘古模型网络多维度混合并行解析 分布式故障恢复 | 9 | 单机多卡训练 分布式训练开始 使用 FleetAPI 进行分布式训练 | 3 | -| 框架设计文档 | Random number generation | 1 | 分散在 API 文档、源码中，其实比较丰富。30+ | 30+ | 设计白皮书 全场景统一 函数式微分编程 动静态图结合 异构并行训练 分布式并行 中间表达 MindIR 高性能数据处理引擎 图算融合加速引擎 二阶优化 可视化调试调优 安全可信 术语 | 13 | | 0 | -| 其它 | Integrated gradients Uncertainty quantification with SNGP Probabilistic regression Keras 一级标题下的 13 篇文章 Thinking in TensorFlow 2 Data input pipelines 一级标题下的 3 篇 GPU TPU | 20 | Learn the Basics Quickstart Deep Learning with PyTorch: A 60 Minute Blitz Building a Convolution/Batch Norm fuser in FX Building a Simple CPU Performance Profiler with FX Channels Last Memory Format in PyTorch Forward-mode Automatic Differentiation Using the PyTorch C++ Frontend Dynamic Parallelism in TorchScript Autograd in C++ Frontend Static Quantization with Eager Model in PyTorch | 11 | 基本介绍 快速入门 进阶案例：线性拟合 混合精度 梯度累积算法 自适应梯度求和算法 降维训练算法 | 7 | 10 分钟快速上手飞桨 使用线性回归预测波士顿房价 模型导出 ONNX 协议 飞桨产品硬件支持表 昆仑 XPU 芯片运行飞桨 海光 DCU 芯片运行飞桨 昇腾 NPU 芯片运行飞桨 环境变量 FLAGS 下 9 篇 hello paddle：从普通程序走向机器学习程序 通过 AutoEncoder 实现时序数据异常检测 广播介绍 自动混合精度训练 梯度裁剪 升级指南 | 20+ | - -可以看除，PaddlePaddle 在文档上是比较完备的，在本文划分的 19 个具体领域中的 17 个领域中都已有文档，包括： - -- 基本数据（Tensor）和基本算子 -- 数据加载与预处理 -- 如何组网 -- 如何训练 -- 保存与加载模型 -- 可视化、调优技巧 -- 自动微分 -- 动态图与静态图 -- 部署相关 -- CV 领域相关 -- NLP 领域相关 -- 推荐领域相关 -- 强化学习领域相关 -- 移动端相关 -- 框架之间的迁移相关 -- 自定义算子 -- 分布式训练 - -并且，与其它框架相比，有一些独特的文章，它们归类到了“其它”中，比如与国产芯片的适配，Paddle 的环境变量的设置及意义等。 - -但是在个别领域，还比较薄弱甚至空缺，主要是： - -- 语音领域是空缺的 -- 与 PyTorch 相比，分布式领域的文档还比较薄弱。PyTorch 有 13 篇分布式有关的文档，涵盖了分布式训练的基本概念、设备之间的通信、不同模块的使用、不同分布式策略的选择和使用。而 Paddle 只有三篇，并且主要集中在 API 接口的介绍，没有结合分布式策略详细介绍，对于没有太多分布式训练经验的人而言，容易困惑。 -- 框架设计文档在官方网站上是空缺的，像 PyTorch、TensorFlow 都有深度介绍框架设计的文档，这对像参与开源建设或者自定义框架的高阶用户，还是很必要的。 - -### 行文风格分析 - -整体而言，PaddlePaddle 的文档保持了较高的水准。不过因为精力有限，笔者虽然有较长的 Paddle 使用经验，但是也没有对所有文档做“深入分析”，在此仅针对一些有代表的案例，发表自己的看法，讨论下是否有改进的必要。 - -**代码风格的统一** - -首先，我认为应该在形式上，尽量追求统一，形式上的“统一美感”，可以给读者留下好的第一印象。 - -文章中有些地方出现了代码高亮格式的不一致，甚至是同一篇文章中就有高亮风格不一致的地方，如 [自定义指标](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/07_customize_cn.html#zidingyizhibiao) 一文中，一些代码中 docstring 有高亮，一些没有： - -![](./images/code-style-01.png) -![](./images/code-style-02.png) - -**标题的统一** - -其次，有些二级标题，在导航栏的显示名称，与导航页面的显示名称，是不一致的： - -![](./images/inconsistent-title.png) - -**文章的新旧更迭** - -还有一些文章的链接，已经失效了，比如 [本地开发指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/10_contribution/local_dev_guide_cn.html) 中： - -![](./images/invalid-url.png) - -有些文章中引用的链接，虽然没有失效，但是其实不应该引用，或者需要做更新。比如 [使用动转静完成以图搜图](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/jit/image_search_with_jit.html) 一文中引用了 [基于图片相似度的图片搜索](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/cv_case/image_search/image_search.html)，但是后者其实已经是一篇“孤岛文章”，并没有被纳入到 Paddle 的文档体系中： - -![](./images/outdated-article.png) - -很可能应该被 [基于图片相似度的图片搜索](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/image_search.html) 替换。 - -这类问题，仔细找的话还可以找到不少，不过定位所有这类问题，超出了本文的范围，建议可以组织人手做一次系统的摸排。 - -**空格的细节** - -中文和英文之间，是否有空格，应该没有严格的规定，但是在官方文档中，应该统一，要么都有空格，要么都没有。如果混用的话，也会有不协调的感觉。 - -![](./images/blanks.png) - - -### 内容分析 - -#### 概述 - -按照 [The documentation system](https://documentation.divio.com/) 中对文档的划分（见本文开头图片），每类文档都有自己的特点和写作标准。而相邻区间的文档，又因为有相似的性质，所以容易“混乱”。 - -比如 Tutorial 和 Guide 是两类相似，但是又不同的文档。一般而言，Tutorial 的风格更像一堂课，需要动手操作 steps，也需要介绍“知识概念”。而 Guide 是解决问题为导向的，需要一系列的 steps，以及较为直接的解决问题。 - -如果在 Guide 中引入了太多的“知识概念”，会让急于解决具体问题的读者，感到重点不明显。 - -而如果在 Tutorial 中，不顾及读者的已有知识体系，单纯地“灌输”操作步骤，就容易让读者失去阅读和学习的乐趣，过早地产生放弃继续阅读的想法。 - -再比如，Reference 和 Guide 也是相邻的象限，不过 Guide 更侧重实际操作，而 Reference 注重理论知识。如果不注意它们的区分，比如在 Guide 中只介绍接口参数，却不举具体的接口使用的例子，容易让读者“心中没底”；在 Reference （如 API 文档）中如果不追求 “dry & clean” 的风格，以及完备的信息，就容易让读者怀疑资料的权威性。 - - -因为 Paddle 中的一些文章，并没有显式地说明到底是哪种类型，除了 API 文档明显应该属于 Reference 外，其它的笔者只能根据自己的感受主观上判断，并且笔者认为几乎均为 Tutorial 或者 Guide 类别，而没有显式的 Explanation 类别的文档。以下内容依据 Tutorial 或 Guide 的要求，来做具体案例分析，不一定准确，仅供参考。 - -#### 具体案例 - -Paddle 当前文档中的 Tutorial，特别是初级内容部分，都很好的遵循了 Tutorial 的写作要求，如 [基本概念](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/index_cn.html) 中的所有文章和 [模型开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/index_cn.html) 中的大部分文章。 - -但是，对于一些类别为 Guide 的文章，往往还有提升空间。以一级标题 [动态图转静态图](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/index_cn.html) 中的文章为例，我们继续讨论： - -**不必要的拆黑箱** - -在 [使用样例](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/basic_usage_cn.html) 一文中，除了提供直接可运行的 steps 之外，还提供了对于用户而言，比较复杂的 “@to_static 的基本执行流程如下图”： - -![](https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/to_static_train.png) - -但是这个流程图个人认为可以在此省去，或者直接移动到 [转换原理](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/principle_cn.html) 一文中。 - -**缺乏 steps** - -在 [支持语法](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/principle_cn.html) 一文中，几乎都是大量罗列，而比较缺少代码的实例。 - -当然，静态图所支持的语法限制，本身就难“正向”做展示。在这里，我建议可以考虑，干脆“反其道行之”，展示“反面操作”，即如果使用的语法超出了静态图的范围，会报怎么样的错误，如何纠正。这样的“做中学”，可能可以给读者更深的印象。而将现有的这种大而全的正想罗列，放入到 API 文档中。 - -**缺简单易复现的环境** - -在 [报错调试](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/debugging_cn.html) 一文中，细致地教用户如何看错误信息，以及介绍相关的多种工具，这都是非常友好的。但是美中不足，并没有配套的仓库。如果能为用户专门准备一个配套的“试错仓库”，用户克隆后，可以直接复现这类错误，并且根据文档定位、纠正，这样可能会更友好。 - - - - -### 组织结构分析 - -从组织结构角度分析，以我个人的感受，飞桨还有一些值得讨论，和可能改进的地方。 - -#### 从 Paddle 文档一级标题看完备性分析 - -在上文 [完备性分析](#完备性分析) 部分，已经 Paddle 文档体系覆盖内容的分析。在本节，笔者尝试讨论，如何对文档的标题做重构，得到更好的文档结构，个人看法，仅供参考。之所以认为这样更合理，是可以为读者提供“一站式”服务，而现有的 Paddle 文档体系，同类别的文档，有时会分散在多个栏目中；或者某个重要的内容，藏的目录级别较深（如 “整体介绍/升级指南/使用高层 API”），不容易被读者发现。 - -建议，可以分为以下几个 “栏目”，每个栏目中有若干篇文章（一级标题）。 - -- 使用 Paddle -- Paddle 与 CV -- Paddle 与 NLP -- Paddle 与语音 -- Paddle 与推荐系统 -- Paddle 与强化学习 -- 自定义扩展 Paddle -- 框架设计文档 -- Cookbook - -它们与现有文档体系的对应关系，以下分开阐述。 - -**使用 Paddle** - -在这个栏目下，针对上手 Paddle 的用户，分模块介绍 Paddle 的各个功能。可以包括： - -- 快速上手（对应现有的《10 分钟快速上手飞桨》，同时应注意链在文章内连接重构后的其它更细节的文章） -- Tensor（对应现有的《Tensor 概念介绍》） -- 数据加载与预处理（对应现有的《数据集定义和加载》、《数据预处理》） -- 搭建模型（对应现有的《Paddle 中的模型与层》、《模型组网》） -- 模型训练（对应现有的《训练与预测验证》、《自定义指标》 -- 自动微分（对应现有的《自动微分机制介绍》） -- 转静态图（对应现有的《动态图与静态图一栏》） -- 模型的保存与加载（对应现有的《模型的保存于加载》） -- 分布式训练（对应现有的《单机多卡训练》、《分布式训练快速开始》、《使用 FleetAPI 进行分布式训练》 - -**Paddle 与 CV** - -对应现有 [计算机视觉](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/image_classification.html) 与 [目标检测 YOLOv3](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3106208) 中等的内容。 - -**Paddle 与 NLP** - -对应现有文档中的 [自然语言处理](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2504863) 与 [持续学习语义理解框架](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2538222)等内容。 - -**Paddle 与语音** - -这一部分 Paddle 尚缺少，可以参考 PyTorch 的文档 https://pytorch.org/tutorials/beginner/audio_io_tutorial.html - -**Paddle 与推荐系统** - -对应现有 [推荐系统](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2200430) 中相关文档。 - -**Paddle 与强化学习** - -对应现有文档 [强化学习](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/reinforcement_learning/index_cn.html) 中相关文档。 - -**自定义扩展 Paddle** - -对应现有文档中 [自定义算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/index_cn.html) 中相关文档。且其中的 “Kernel Primitive API” 中的文章，可以上升一个级别。 - -**框架设计文档** - -现有 Paddle 文档欠缺这类文档，可以参考 Mindspore 的文档补充 https://www.mindspore.cn/docs/zh-CN/r1.7/design/technical_white_paper.html。 - -**Cookbook** - -建议将现有的已经足够丰富程独立产品的文档，分离出去，变为独立产品的文档（如部署相关的文档）。 - -将内容不够丰富，又不方便分类的“其它”类型的文档，做成 cookbook（具体名字可以讨论，这里主要是参考的 Python Cookbook 一书），意味着以解决问题为导向的实践指南。 - -比如现有的《自动混合精度训练》、《梯度剪裁方式介绍》、《飞桨模型量化》、《模型导出 ONNX》 等，可以放在这一个栏目。 - -#### “整体介绍”栏目下的结构问题 - -首先是 [整体介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/index_cn.html) 里的目录结构和内容可以考虑调整。 - -如 [整体介绍/基本概念](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/index_cn.html) 中包括了以下内容： - -- Tensor 概念及介绍 -- Paddle 中的模型与层 -- 广播介绍 -- 自动微分 -- 自动混合精度训练 -- 梯度裁剪 - -这部分应该是面对想要了解飞桨并上手使用的初级用户准备的。所以它定位应该是 “Tutorial”。但是有些地方可能讨论改进： - -- [Paddle 中的模型与层](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_cn.html) 一节与“升级指南” 里的 [组网方式](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#sequential) 一节有内容重叠。 -- [广播介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/broadcasting_cn.html) 更像是 “Explanation” 类的东西，而且内容比较少，可以考虑放置到 API 文档中，以链接的方式引用就可以了。 -- [自动微分机制介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/autograd_cn.html) 一节的内容与 [PyTorch 的 Tutorial](https://pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html) 相比，在介绍 “飞桨自动微分运行机制” 时感觉内容选题复杂了一点。而在“飞桨中自动微分相关所有的使用方法说明” 一节中，没有继续细分“计算梯度”，“关闭梯度”等小标题，作为 Tutorial 感觉友好度还可以加强 -- [自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html) 和 [梯度剪裁](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.html) 两节内容感觉归为 Guide 更合理，放在“基本概念”中会有点学习曲线陡增的感觉。 - -其次，在 [升级指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html) 包含了这些内容： - -- 数据处理 -- 组网方式 -- 模型训练 -- 单机多卡启动 -- 模型保存 -- 推理 - -这里面包含了很多“干货”的内容，但是我觉得放在“升级指南”中有“明珠暗投”的风险。因为一般人对“升级指南”的刻板印象，应该类似是“relase log”级别的东西，可能会不会优先点开。 - -据我体验，升级指南中的 [使用高层 API](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#id4) 和 [使用基础 API](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#id5) 包含了其它地方没有的内容，这些内容被用户错过的话，是可惜的。 - -而升级指南中的 [单机多卡启动](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#danjiduokaqidong) 和 [分布式训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/index_cn.html) 中的内容非常接近，感觉可以考虑合并。 - -我建议可以对整体介绍中的内容进行重新组织，主要方向有： - -- “整体介绍部分”尽量瘦身，主要以“信息索引”存在 -- 将 “Tensor 概念介绍”、“模型与层”以及上文提到的升级指南中的操作性强又基础的内容，考虑与一级目录 [模型开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/index_cn.html) 合并 -- 将自动混合精度、梯度剪裁以及分布式训练有关的内容，整理为专门领域的 Guide，作为独立的一级目录或二级目录存在 - - -#### 其它一级目录 - -除了上文提到的个人看法外，飞桨的一级目录，大部分都内容充实、结构合理。只是对于少数章节，我个人觉得可以再讨论改进。 - -[性能调优](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/performance_improving/index_cn.html) 作为一个一级目录存在，但其中的二级目录只有 [模型量化](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/performance_improving/quantization.html) 一文。这显得有些单薄。不过在现有的目录结构下，这种选择可能已经是最优的了。我还没有具体的改进建议。 - -但是，如果未来考虑增加新栏目的话，感觉可以把增加一个“开发者实践”类型的栏目，其内容可以参考 MindSpore 的 [开发者精华分享](https://bbs.huaweicloud.com/forum/forumdisplay-fid-1076-orderby-lastpost-filter-typeid-typeid-1255sub.html)。将一些任务导向的话题，如量化、profiling 等“杂项”放在其中。 - -其次 [环境变量](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/flags/flags_cn.html) 这种纯粹的 flags 说明，应该归类为 Reference，感觉将它直接放到 API 文档中可能会更合理。 - -#### 与《教程》栏目的大量重叠 - -[教程](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3465990) 一栏下包括了三类个子栏目： - -- 零基础实践深度学习 -- 产业级实践深度学习 -- 飞桨产业实践范例库 - -这些内容在 [PaddlePaddle/docs](https://github.com/PaddlePaddle/docs/) 仓库似乎找不到，有可能独立于 [文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) 团队之外的另一个团队做的。 - -但是从用户的角度看，它们之间有大量的内容重叠，比如： - -- [使用飞桨重写波士顿房价预测任务](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3469365) VS [使用线性回归预测波士顿房价](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/linear_regression.html) -- [分布式训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2538251) VS [分布式训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/index_cn.html) -- [外部用户如何贡献代码](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2538259) VS [参与开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/10_contribution/index_cn.html) -- ... - -它们有些是内容上就是同质的，有些是标题上比较接近（但是内容风格不一致）。我建议如果能更加清晰地将他们做“差异化”处理，可能会更好。 - -## 用户角色代入分析 - -### 初级用户 - -初级用户可以通过 [整体介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)、[模型开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/index_cn.html) 上手飞桨，掌握从数据加载、预处理到模型训练、导出到 ONNX 等各项基本技能。 - -### 中级用户 - -中级用户可以通过 [VisualDL 工具](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/03_VisualDL/index_cn.html)、[动态图转静态图](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/03_VisualDL/index_cn.html)、[推理部署](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/index_cn.html)、以及 [应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/index_cn.html)、[分布式训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/index_cn.html) 中的各类信息，掌握实际落地过程中各个领域的遇到的模型调试、性能调优、部署等问题。 - -### 高级用户 - -想为飞桨开源项目增加功能的用户，可以参考 [自定义算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/index_cn.html) 了解如何增加算子，以及 [参与开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/10_contribution/index_cn.html) 了解提交 PR 的流程。 - -不过，其它框架，一般会有文档介绍它组件中的设计理念和原理，如 PyTorch 的这篇 [autograd](https://pytorch.org/docs/stable/notes/autograd.html)，OneFlow 的一些 [技术博客](https://zhuanlan.zhihu.com/p/337851255)，以及 MindSpore 的 [设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/design/technical_white_paper.html) （这个信息量偏薄弱点）。 - -在 Paddle 的文档体系中，好像没有找到类似的内容，这对于想学习 Paddle 底层原理甚至参与开源建设的爱好者，是一种遗憾。希望可以考虑推出。 - -## 报告总结 - -飞桨文档的完备性、质量和组织结构都已经达到了很高的水准，但是在此分析过程中依然发现一些可以讨论、改进的点，包括： - -- 完备性方面：可加强语音、分布式、设计文档方面的内容 -- 组织结构方面：一些二级标题升级为一级标题；与“教程”中重叠内容较多的文章，考虑加强区分标志 -- 行文风格方面：建议对行文形式上做出更显式的要求（如中英文之间的空格），并做一次彻底的摸排 -- 内容方面：Tutorial 类别质量已经很高，Guide 类别的部分文章可能存在“不必要的知识过多”，“实践性不足”，“配套不完善”的问题，期待改进。 - -个人非常期待飞桨能整理开放出设计文档，让更多人可以了解飞桨开源代码背后的原理和巧妙。 diff --git a/docs/eval/images/blanks.png b/docs/eval/images/blanks.png deleted file mode 100644 index dc67d0f7133f6ec4acce906f322aa9c0b8751fc9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 26233 zcmeGDc|4Tg8$XVZ6e&xkNVXKoUWBYeUP3~VEo%u`hU~^NBa+IRov}pLFm_`dr0iR^ zF$P17Z5ZodW*Fbm`}6qyK7W7z{(3xc-;Z<7b*}4qUFV$px}Nv_`I){J%Y_>k0001s zj`n|s004a-?fURM1MM&7!23S!ADx$>)*}FXkY|;4bJkH^PaOcLjJKyH!>7BN@ z7XZN8ar#Tw!7Xgo;XN<$ 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+0,0 @@ -############ -参与开发 -############ - -.. toctree:: - :maxdepth: 1 - - local_dev_guide_cn.md - submit_pr_guide_cn.md - faq_cn.rst diff --git a/docs/guides/10_contribution/index_en.rst b/docs/guides/10_contribution/index_en.rst deleted file mode 100644 index aad872200f5..00000000000 --- a/docs/guides/10_contribution/index_en.rst +++ /dev/null @@ -1,9 +0,0 @@ -################################# -How to contribute codes to Paddle -################################# - -.. toctree:: - :maxdepth: 1 - - local_dev_guide_en.md - submit_pr_guide_en.md diff --git a/docs/guides/10_contribution/rfcs.md b/docs/guides/10_contribution/rfcs.md deleted file mode 100644 index b7d84ee1099..00000000000 --- a/docs/guides/10_contribution/rfcs.md +++ /dev/null @@ -1,67 +0,0 @@ -功能名称： (填写任务的名称, 在 Paddle 中实现 Common Subexpression Elimination（公共子表达式删除）的图优化 pass) - -开始日期：（填写提交时候的日期，YYYY-MM-DD） - -RFC PR：[PaddlePaddle#33333]() - -GitHub Issue：[PaddlePaddle#35993](https://github.com/PaddlePaddle/Paddle/issues/35993) - -# 总结 -一段关于该功能的解释。 - -# 动机 -我们为什么要这样做？它支持哪些用例？预期的结果是什么？ - -# 使用指南级别的说明 -解释这个 rfc，就好像它已经包含在 PaddlePaddle 中，而且你正在教一个 PaddlePaddle 用户。 - -这通常意味着。 - -- 引入新的命名概念。 -- 解释该功能能够实现什么（提示：用例子来思考）。 -- 如果适用，提供错误信息样本、废弃警告或迁移指导。 -- 对于新功能的 RFC，这部分应该提供一个以实例为导向的介绍，并具体解释其影响。 - -# 参考文献级别的说明 -这是 RFC 的技术部分。对这个设计进行足够详细的解释，即 - -- 它与其他功能的交互是清楚的。 -- 合理地清楚该功能将如何实现。 -- 通过实例剖析 corner cases。 -- 该部分应该回到上一节给出的例子，并更充分地解释详细的建议如何使这些例子发挥作用。 - -# 缺点 -为什么我们不应该这样做？ - -# 理论依据和替代方案 -为什么这个设计在可能的设计思路中是最好的？ -还有哪些设计被考虑过，不选择这些设计的理由是什么？ -不这样做的影响是什么？ - -# 现有技术 -讨论现有技术，包括好的和坏的，与本 rfc 有关的。可以包括以下几个例子。 - -- 这个功能在其他深度学习框架中是否存在，讨论他们的社区所做的实验？ -- 对于社区建议。这个功能是否由其他社区完成，他们的经验是什么？ -- 对于其他团队来说。我们可以从其他社区所做的事情中吸取什么教训？ -- 论文。是否有任何已发表的论文或帖子来讨论这个问题？如果你有一些相关的论文可以参考，这可以作为一个更详细的理论背景。 -- 如果没有先例，那也没关系--无论你的未来的可能性想法是全新的还是从其他语言改编的，对我们来说都是有趣的。 - -请注意，虽然其他深度学习框架的先例是某种动力，但它本身并不能成为 RFC 的动力。也请考虑到 PaddlePaddle 有意与其他深度学习框架有所区别。 - -# 未解决的问题 - -- 在合并前，你希望通过 RFC 程序解决设计中的哪些部分？ -- 在功能稳定前，你希望通过实现这个功能来解决哪些部分的设计问题？ -- 你认为哪些相关问题超出了本 RFC 的范围，可以在未来独立于本 RFC 的解决方案来解决？ - -# 未来的可能性 -想一想你的 RFC 的自然延伸和演变，以及它将如何全面影响项目。试着把这部分作为一个工具，在你的 RFC 中更全面地考虑与框架所有可能的互动。还要考虑这一切如何与项目和相关的子团队的路线图相适应。 - -这也是一个 "倾倒想法 "的好地方，如果这些想法超出了你所写的 RFC 的范围，但又与之相关。 - -如果你已经尝试过，但想不出任何未来的可能性，你可以简单地说，你什么都想不出来。 - -请注意，在未来可能性部分写下一些东西并不是接受当前或未来 RFC 的理由；这种说明应该放在本篇或后续 RFC 的动机或理由部分。该部分只是提供额外的信息。 - -参考：[tvm/rfc-template](https://github.com/apache/tvm-rfcs/blob/main/0000-template.md) From 907178a9aaa59f8a6d2fa95d23293a23f0753af7 Mon Sep 17 00:00:00 2001 From: hu-qi Date: Sun, 14 Apr 2024 02:01:03 +0800 Subject: [PATCH 2/3] remove some. --- .../analysis_tools/benchmark_cn.md | 90 ------- .../analysis_tools/cpu_profiling_cn.md | 183 -------------- .../analysis_tools/cpu_profiling_en.md | 224 ------------------ .../host_memory_profiling_en.md | 87 ------- .../analysis_tools/index_cn.rst | 18 -- .../analysis_tools/index_en.rst | 18 -- .../analysis_tools/nvvp1.png | Bin 426047 -> 0 bytes .../analysis_tools/nvvp2.png | Bin 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验证指标略有不同，本文只介绍训练阶段的指标验证。训练阶段关注的是模型训练集上的精度，训练集是完备的，因此关注大 batch\_size 下的训练速度,关注吞吐量，例如图像模型常用的 batch\_size=128, 多卡情况下会加大；预测阶段关注的是在测试集上的精度，线上服务测试数据不能提前收集，因此关注小 batch\_size 下的预测速度，关注延迟，例如预测服务常用的 batch\_size=1, 4 等。 - -[Fluid](https://github.com/PaddlePaddle/Paddle>)是 PaddlePaddle 从 0.11.0 版本开始引入的设计，本文的基准测试在该版本上完成。 - - -环境搭建 -======== - -基准测试中模型精度和硬件、框架无关，由模型结构和数据共同决定；性能方面由测试硬件和框架性能决定。框架基准测试为了对比框架之间的差异，控制硬件环境，系统库等版本一致。下文中的对比实验都在相同的硬件条件和系统环境条件下进行. - - -不同架构的 GPU 卡性能差异巨大，在验证模型在 GPU 上训练性能时，可使用 NVIDIA 提供的命令:```nvidia-smi``` 检验当前使用的 GPU 型号，如果测试多卡训练性能，需确认硬件连接是 [nvlink](https://zh.wikipedia.org/zh/NVLink)或 [PCIe](https://zh.wikipedia.org/zh-hans/PCI_Express)。 同样地，CPU 型号会极大影响模型在 CPU 上的训练性能。可读取`/proc/cpuinfo`中的参数，确认当前正在使用的 CPU 型号。 - -下载 GPU 对应的 Cuda Tool Kit 和 Cudnn，或者使用 NVIDIA 官方发布的 nvidia-docker 镜像 [nvidia-docker](https://github.com/NVIDIA/nvidia-docker), 镜像内包含了 Cuda 和 Cudnn，本文采用这种方式。 Cuda Tool Kit 包含了 GPU 代码使用到的基础库，影响在此基础上编译出的 Fluid 二进制运行性能。 - -准备好 Cuda 环境后，从 github 上下载 Paddle 代码并编译，会生成对应的最适合当前 GPU 的 sm\_arch 二进制[sm\_arch](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html)。另外，cudnn 对卷积类任务影响巨大，在基准测试中需要小版本一致，例如 Cudnn7.0.2 与 Cudnn7.1.4 在 Resnet 上有 5%以上差异。 - - -选择基准模型 -============ - -对框架做基准测试，需要覆盖不同训练任务和不同大小的模型，本文中选取了图像和 NLP 的最为常用的 5 个模型。 - -任务种类| 模型名称| 网络结构| 数据集 -:---:|:--:|:---:|:---: -图像生成| CycleGAN| GAN| horse2zebra -图像分类| SE-ResNeXt50| Resnet-50| image-net -语义分割| DeepLab_V3+| ResNets| cityscapes -自然语言| Bert| Transformer| Wikipedia -机器翻译| Transformer| Attention| Wikipedia - -CycleGAN, SE-ResNeXt50, DeepLab_V3+属于 CNN 模型, Bert, Transformer 是一种比传统 RNN 模型更好的 NLP 模型。 -[benchmark](https://github.com/PaddlePaddle/Paddle/tree/develop/benchmark/fluid) -基准模型测试脚本中，均跳过了前几个 batch 的训练过程，原因是加载数据和分配显存受系统当前运行情况影响，会导致统计性能不准确。运行完若干个轮次后，统计对应指标。 - - -基准模型的数据的选择方面，数据量大且验证效果多的公开数据集为首选。图像模型 CycleGAN 选择了 horse2zebra 数据集，SE-ResNeXt50 选择了[image-net](http://www.image-net.org/challenges/LSVRC/2012/nnoupb)数据集，图像大小预处理为和 Imagenet 相同大小，因此性能可直接对比。 -NLP 模型的公开且影响力大数据集较少，Bert 和 Transformer 模型都选择了[Wikipedia](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2)数据集。 - - -注意，图像模型每条样本大小相同，图像经过变换后大小一致，因此经过的计算路径基本相同，计算速度和显存占用波动较小，可以从若干个 batch 的数据中采样得到当前的训练性能数据。而 NLP 模型由于样本长度不定，计算路径和显存占用也不相同，因此只能完整运行若干个轮次后，统计速度和显存消耗。 -显存分配是特别耗时的操作，因此 Fluid 默认会占用所有可用显存空间形成显存池，用以加速计算过程中的显存分配。如果需要统计模型真实显存消耗，可设置环境变量`FLAGS_fraction_of_gpu_memory_to_use=0.0`，观察最大显存开销。 - - -测试过程 -======== - -- GPU 单机单卡测试 - -本教程使用了 Cuda9, Cudnn7.0.1。来源为:```nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04``` - -``` - nvidia-docker run -it --name CASE_NAME --security-opt seccomp=unconfined -v $PWD/benchmark:/benchmark -v /usr/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu paddlepaddle/paddle:latest-dev /bin/bash -``` -在单卡上测试，设置 CUDA 的环境变量使用一块 GPU，``CUDA_VISIBLE_DEVICES=0`` -然后代码中设置为使用 CUDAPlace，如果使用 Paddle 代码库中的脚本，只需要命令行参数传入 use_gpu=True 即可。 - -``` - >>> import paddle.fluid as fluid - >>> place = fluid.CUDAPlace(0) // 0 指第 0 块 GPU -``` - -测试结果 -======== - -本教程对比相同环境下的 Fluid1.4, PyTorch1.1.0 和 TensorFlow1.12.0 的性能表现。 -硬件环境为 CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, GPU: Tesla v100(volta) 21729MiB x 1, Nvidia-Driver 384.66。 -系统环境为 Ubuntu 16.04.3 LTS, 本文中采用了 docker 环境，系统版本为 nvidia-docker17.05.0-ce。 -测试的 Fluid 版本为[v.1.4.1](https://github.com/PaddlePaddle/Paddle/tree/v1.4.1) 。 -TensorFlow 版本为[v.1.12.0-rc2](https://github.com/tensorflow/tensorflow/tree/v1.12.0-rc2)。 -PyTorch 版本为[v.1.1.0](https://github.com/pytorch/pytorch/tree/v1.1.0)。 -使用的脚本和配置见[benchmark](https://github.com/PaddlePaddle/Paddle/tree/develop/benchmark/fluid) 。 -SE-ResNeXt50 对比的框架是 PyTorch，因为 TensorFlow 上没有对应的模型。 -图表中统计单位为 samples/秒。 - - - -- GPU 单机单卡测试结果 - - Model|Fluid GPU| TensorFlow/PyTorch GPU - :---:|:--:|:---: - CycleGAN| 7.3 samples/s| 6.1 samples/s - SE-ResNeXt50| 169.4 samples/s | 153.1 samples/s - DeepLab_V3+| 12.8 samples/s | 6.4 samples/s - Bert| 4.0 samples/s | 3.4 samples/s - Transformer| 4.9 samples/s | 4.7 samples/s diff --git a/docs/guides/performance_improving/analysis_tools/cpu_profiling_cn.md b/docs/guides/performance_improving/analysis_tools/cpu_profiling_cn.md deleted file mode 100644 index 8b9c492f162..00000000000 --- a/docs/guides/performance_improving/analysis_tools/cpu_profiling_cn.md +++ /dev/null @@ -1,183 +0,0 @@ -# CPU 性能调优 - -此教程会介绍如何使用 Python 的 cProfile 包、Python 库 yep、Google perftools 来进行性能分析 (profiling) 与调优（performance tuning）。 - -Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。 - -PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大部分 Python API 调用用 C++ 写的 libpaddle.so。所以 PaddlePaddle 的性能分析与调优分为两个部分: - -* Python 代码的性能分析 -* Python 与 C++ 混合代码的性能分析 - - -## Python 代码的性能分析 - -### 生成性能分析文件 - -Python 标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成 Python 性能分析的命令如下: - -```bash -python -m cProfile -o profile.out main.py -``` - -其中 `main.py` 是我们要分析的程序，`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印到标准输出。 - -### 查看性能分析文件 - -`cProfile` 在 main.py 运行完毕后输出`profile.out`。我们可以使用[`cprofilev`](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个 Python 的第三方库。使用它会开启一个 HTTP 服务，将性能分析结果以网页的形式展示出来： - -```bash -cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py -``` - -其中`-a`标识 HTTP 服务绑定的 IP。使用`0.0.0.0`允许外网访问这个 HTTP 服务。`-p`标识 HTTP 服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。 - -用 Web 浏览器访问对应网址，即可显示性能分析的结果： - -``` - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.284 0.284 29.514 29.514 main.py:1() - 4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/executor.py:20(run) - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14() -``` - -每一列的含义是: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
列名含义
ncalls 函数的调用次数
tottime 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间
percall tottime 的每次调用平均时间
cumtime 函数总时间。包含这个函数调用其他函数的时间
percall cumtime 的每次调用平均时间
filename:lineno(function) 文件名, 行号，函数名
- - -### 寻找性能瓶颈 - -通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。 - -将性能分析结果按照 tottime 排序，效果如下: - -```text - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader) - 107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:219(__init__) - 4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) - 1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/__init__.py:1() -``` - -可以看到最耗时的函数是 C++端的`run`函数。这需要联合我们第二节`Python`与`C++`混合代码的性能分析来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。 - -```text -Called By: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> - -Function was called by... - ncalls tottime cumtime -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:487(clone) - 1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:534(append_backward) - - -Called: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> -``` - -通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。 - - - -## Python 与 C++混合代码的性能分析 - -### 生成性能分析文件 - -C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试 Python 中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而 Python 的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行 Python 与 C++混合代码的性能分析 - -使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu 下安装命令为 - -```bash -apt update -apt install libgoogle-perftools-dev -pip install yep -``` - -安装完毕后，我们可以通过 - -```bash -python -m yep -v main.py -``` - -生成性能分析文件。生成的性能分析文件为`main.py.prof`。 - -命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为 C++与 Python 不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施: - -1. 编译时指定`-g`生成调试信息。使用 cmake 的话，可以将 CMAKE_BUILD_TYPE 指定为`RelWithDebInfo`。 -2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。 -3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭 openmp 优化。 - -### 查看性能分析文件 - -在运行完性能分析后，会生成性能分析结果文件。我们可以使用[`pprof`](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有 web 服务界面，且展示效果更好。 - -安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下: - -```bash -go get github.com/google/pprof -``` - -进而我们可以使用如下命令开启一个 HTTP 服务: - -```bash -pprof -http=0.0.0.0:3213 `which python` ./main.py.prof -``` - -这行命令中，`-http`指开启 HTTP 服务。`which python`会产生当前 Python 二进制的完整路径，进而指定了 Python 可执行文件的路径。`./main.py.prof`输入了性能分析结果。 - -访问对应的网址，我们可以查看性能分析的结果。结果如下图所示: - -![result](./pprof_1.png) - - -### 寻找性能瓶颈 - -与寻找 Python 代码的性能瓶颈类似，寻找 Python 与 C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。 - -例如下图中， - -![kernel_perf](./pprof_2.png) - -在一次训练中，乘法和乘法梯度的计算占用 2%-4%左右的计算时间。而`MomentumOp`占用了 17%左右的计算时间。显然，`MomentumOp`的性能有问题。 - -在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。 diff --git a/docs/guides/performance_improving/analysis_tools/cpu_profiling_en.md b/docs/guides/performance_improving/analysis_tools/cpu_profiling_en.md deleted file mode 100644 index 216694965b3..00000000000 --- a/docs/guides/performance_improving/analysis_tools/cpu_profiling_en.md +++ /dev/null @@ -1,224 +0,0 @@ -# Tune CPU performance - -This tutorial introduces techniques we use to profile and tune the -CPU performance of PaddlePaddle. We will use Python packages -`cProfile` and `yep`, and Google's `perftools`. - -Profiling is the process that reveals performance bottlenecks, -which could be very different from what's in the developers' mind. -Performance tuning is done to fix these bottlenecks. Performance optimization -repeats the steps of profiling and tuning alternatively. - -PaddlePaddle users program AI applications by calling the Python API, which calls -into `libpaddle.so.` written in C++. In this tutorial, we focus on -the profiling and tuning of - -1. the Python code and -1. the mixture of Python and C++ code. - -## Profiling the Python Code - -### Generate the Performance Profiling File - -We can use Python standard -package, [`cProfile`](https://docs.python.org/2/library/profile.html), -to generate Python profiling file. For example: - -```bash -python -m cProfile -o profile.out main.py -``` - -where `main.py` is the program we are going to profile, `-o` specifies -the output file. Without `-o`, `cProfile` would outputs to standard -output. - -### Look into the Profiling File - -`cProfile` generates `profile.out` after `main.py` completes. We can -use [`cprofilev`](https://github.com/ymichael/cprofilev) to look into -the details: - -```bash -cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py -``` - -where `-a` specifies the HTTP IP, `-p` specifies the port, `-f` -specifies the profiling file, and `main.py` is the source file. - -Open the Web browser and points to the local IP and the specifies -port, we will see the output like the following: - -``` - ncalls tottime percall cumtime percall filename:lineno(function) - 1 0.284 0.284 29.514 29.514 main.py:1() - 4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/executor.py:20(run) - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14() -``` - -where each line corresponds to Python function, and the meaning of -each column is as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
columnmeaning
ncalls the number of calls into a function
tottime the total execution time of the function, not including the execution time of other functions called by the function
percall tottime divided by ncalls
cumtime the total execution time of the function, including the execution time of other functions being called
percall cumtime divided by ncalls
filename:lineno(function) where the function is define
- -### Identify Performance Bottlenecks - -Usually, `tottime` and the related `percall` time is what we want to -focus on. We can sort above profiling file by tottime: - -```text - 4696 12.040 0.003 12.040 0.003 {built-in method run} - 300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader) - 107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:219(__init__) - 4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) - 1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/__init__.py:1() -``` - -We can see that the most time-consuming function is the `built-in -method run`, which is a C++ function in `libpaddle.so`. We will -explain how to profile C++ code in the next section. At this -moment, let's look into the third function `sync_with_cpp`, which is a -Python function. We can click it to understand more about it: - -``` -Called By: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> - -Function was called by... - ncalls tottime cumtime -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) -/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:487(clone) - 1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/fluid/framework.py:534(append_backward) - - -Called: - - Ordered by: internal time - List reduced from 4497 to 2 due to restriction <'sync_with_cpp'> -``` - -The lists of the callers of `sync_with_cpp` might help us understand -how to improve the function definition. - -## Profiling Python and C++ Code - -### Generate the Profiling File - -To profile a mixture of Python and C++ code, we can use a Python -package, `yep`, that can work with Google's `perftools`, which is a -commonly-used profiler for C/C++ code. - -In Ubuntu systems, we can install `yep` and `perftools` by running the -following commands: - -```bash -apt update -apt install libgoogle-perftools-dev -pip install yep -``` - -Then we can run the following command - -```bash -python -m yep -v main.py -``` - -to generate the profiling file. The default filename is -`main.py.prof`. - -Please be aware of the `-v` command line option, which prints the -analysis results after generating the profiling file. By examining the - the print result, we'd know that if we stripped debug -information from `libpaddle.so` at build time. The following hints -help make sure that the analysis results are readable: - -1. Use GCC command line option `-g` when building `libpaddle.so` so to - include the debug information. The standard building system of - PaddlePaddle is CMake, so you might want to set - `CMAKE_BUILD_TYPE=RelWithDebInfo`. - -1. Use GCC command line option `-O2` or `-O3` to generate optimized - binary code. It doesn't make sense to profile `libpaddle.so` - without optimization, because it would anyway run slowly. - -1. Profiling the single-threaded binary file before the - multi-threading version, because the latter often generates tangled - profiling analysis result. You might want to set environment - variable `OMP_NUM_THREADS=1` to prevents OpenMP from automatically - starting multiple threads. - -### Examining the Profiling File - -The tool we used to examine the profiling file generated by -`perftools` is [`pprof`](https://github.com/google/pprof), which -provides a Web-based GUI like `cprofilev`. - -We can rely on the standard Go toolchain to retrieve the source code -of `pprof` and build it: - -```bash -go get github.com/google/pprof -``` - -Then we can use it to profile `main.py.prof` generated in the previous -section: - -```bash -pprof -http=0.0.0.0:3213 `which python` ./main.py.prof -``` - -Where `-http` specifies the IP and port of the HTTP service. -Directing our Web browser to the service, we would see something like -the following: - -![result](./pprof_1.png) - -### Identifying the Performance Bottlenecks - -Similar to how we work with `cprofilev`, we'd focus on `tottime` and -`cumtime`. - -![kernel_perf](./pprof_2.png) - -We can see that the execution time of multiplication and the computing -of the gradient of multiplication takes 2% to 4% of the total running -time, and `MomentumOp` takes about 17%. Obviously, we'd want to -optimize `MomentumOp`. - -`pprof` would mark performance critical parts of the program in -red. It's a good idea to follow the hints. diff --git a/docs/guides/performance_improving/analysis_tools/host_memory_profiling_en.md b/docs/guides/performance_improving/analysis_tools/host_memory_profiling_en.md deleted file mode 100644 index b1dbee1bd45..00000000000 --- a/docs/guides/performance_improving/analysis_tools/host_memory_profiling_en.md +++ /dev/null @@ -1,87 +0,0 @@ -# Heap Memory Profiling and Optimization - -Any computer program has the danger of memory leak. Generally, **Memory Leak** is caused by the unreleased heap memory allocated by the program. As the memory occupied by the program becomes larger and larger, it will affect the stability of the program, which may make the running speed slower or give rise to OoM(Out of Memory). It even compromises the stability of the machine in use, and leads to *downtime* . - - -There are many memory leak analysis tools at present. Typical ones include, [valgrind](http://valgrind.org/docs/manual/quick-start.html#quick-start.intro), [gperftools](https://gperftools.github.io/gperftools/). - -Because Fluid runs in C++ core driven by Python, It is very difficult for valgrind to analyze directly. You need to compile the debug version and dedicated Python version with valgrind support, and most of the output information is Python's own symbols and call information. In addition, valgrind will make the program run very slowly, so it is not recommended. - -Here we mainly introduce the use of [gperftools](https://gperftools.github.io/gperftools/) . - -gperftool mainly supports four functions: - -- thread-caching malloc -- heap-checking using tcmalloc -- heap-profiling using tcmalloc -- CPU profiler - -Paddle also provides a [tutorial on CPU performance analysis](./cpu_profiling_en.html) based on gperftool. - -For the analysis for heap, we mainly use thread-caching malloc and heap-profiling using tcmalloc. - -## Environment - -This tutorial is based on the Docker development environment paddlepaddle/paddle:latest-dev provided by paddle, based on the Ubuntu 16.04.4 LTS environment. - -## Manual - -- Install google-perftools - -``` -apt-get install libunwind-dev -apt-get install google-perftools -``` - -- Install pprof - -``` -go get -u github.com/google/pprof -``` - -- Configure Running Environment - -``` -export PPROF_PATH=/root/gopath/bin/pprof -export PPROF_BINARY_PATH=/root/gopath/bin/pprof -export LD_PRELOAD=/usr/lib/libtcmalloc.so.4 -``` - -- Use heap profile to run python program. The essence of it is to get a snapshot of the heap allocation periodically. - -``` -# HEAPPROFILE sets the directory and file prefix of the generated heap analysis file -# HEAP_PROFILE_ALLOCATION_INTERVAL Sets how many storage dumps are allocated for each dump, default 1GB -env HEAPPROFILE="./perf_log/test.log" HEAP_PROFILE_ALLOCATION_INTERVAL=209715200 python trainer.py -``` - -As the program runs, a lot of files will be generated in the perf_log folder as follows: - -``` --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0001.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0002.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0003.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0004.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0005.heap --rw-r--r-- 1 root root 1.0M Jun 1 15:00 test.log.0006.heap -``` - -- Analyze the heap files with pprof. There are two modes of analysis: - - Complete mode. An analysis of the current heap is performed, showing some of the call paths for the current allocation of memory. - - ``` - pprof --pdf python test.log.0012.heap - ``` - The command above will generate a file of profile00x.pdf, which can be opened directly, for example, [memory_cpu_allocator](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_cpu_allocator.pdf). As demonstrated in the chart below, during the running of the CPU version fluid, the module CPUAllocator is allocated with most memory. Other modules are allocated with relatively less memory, so they are ignored. It is very inconvenient for inspecting memory leak for memory leak is a chronic process which cannot be inspected in this picture. - ![result](https://user-images.githubusercontent.com/3048612/40964027-a54033e4-68dc-11e8-836a-144910c4bb8c.png) - - - Diff mode. You can do diff on the heap at two moments, which removes some modules whose memory allocation has not changed, and displays the incremental part. - ``` - pprof --pdf --base test.log.0010.heap python test.log.1045.heap - ``` - The generated result: [`memory_leak_protobuf`](https://github.com/jacquesqiao/Paddle/blob/bd2ea0e1f84bb6522a66d44a072598153634cade/doc/fluid/howto/optimization/memory_leak_protobuf.pdf) - - As shown from the figure: The structure of ProgramDesc has increased by 200MB+ between the two versions, so there is a large possibility that memory leak happens here, and the final result does prove a leak here. - - ![result](https://user-images.githubusercontent.com/3048612/40964057-b434d5e4-68dc-11e8-894b-8ab62bcf26c2.png) - ![result](https://user-images.githubusercontent.com/3048612/40964063-b7dbee44-68dc-11e8-9719-da279f86477f.png) diff --git a/docs/guides/performance_improving/analysis_tools/index_cn.rst b/docs/guides/performance_improving/analysis_tools/index_cn.rst deleted file mode 100644 index 1e1a5d1cbe2..00000000000 --- a/docs/guides/performance_improving/analysis_tools/index_cn.rst +++ /dev/null @@ -1,18 +0,0 @@ -.. _api_guide_analysis_tools: - -############### -性能优化分析及工具 -############### - -.. toctree:: - :hidden: - - cpu_profiling_cn.md - host_memory_profiling_cn.md - timeline_cn.md - -本模块介绍 Fluid 使用过程中的调优方法，包括： - -- `CPU 性能调优 `_：介绍如何使用 cProfile 包、yep 库、Google perftools 进行性能分析与调优 -- `堆内存分析和优化 `_：介绍如何使用 gperftool 进行堆内存分析和优化，以解决内存泄漏的问题 -- `Timeline 工具简介 `_ ：介绍如何使用 Timeline 工具进行性能分析和调优 diff --git a/docs/guides/performance_improving/analysis_tools/index_en.rst b/docs/guides/performance_improving/analysis_tools/index_en.rst deleted file mode 100644 index abacd2fb5fe..00000000000 --- a/docs/guides/performance_improving/analysis_tools/index_en.rst +++ /dev/null @@ -1,18 +0,0 @@ -####################################### -Performance Profiling and Optimization -####################################### - -.. toctree:: - :hidden: - - - 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z4DG$X{>bS}w5R|4*9NoaF>R;GUt2Xx9Qf5A8xmz-A5UV+;f4v%h%YVSs}o8W{1wEv z^8K5*q=eW(GujvaVT+i6|9_zW!*B#5FK*y9+Eb)D_ECm38%{Z=RS~P4nqT^mtL`Lk zuMiO)PrdJNzVN>)y7<@PjJk`A3nhw|QUl(sg(LO9zTkPLi|v2qf2G本地使用 - -1. 在训练的主循环外加上`profiler.start_profiler(...)`和`profiler.stop_profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个 profile 的记录文件。 - - **提示：** - 请不要在 timeline 记录信息时运行太多次迭代，因为 timeline 中的记录数量和迭代次数是成正比的。 - - ```python - import numpy as np - import paddle - import paddle.fluid as fluid - from paddle.fluid import profiler - - place = fluid.CPUPlace() - - def reader(): - for i in range(100): - yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')], - - main_program = fluid.Program() - startup_program = fluid.Program() - - with fluid.program_guard(main_program, startup_program): - data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2]) - data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3]) - out = fluid.layers.fc(input=[data_1, data_2], size=2) - # ... - - feeder = fluid.DataFeeder([data_1, data_2], place) - exe = fluid.Executor(place) - exe.run(startup_program) - pass_num = 10 - - for pass_id in range(pass_num): - for batch_id, data in enumerate(reader()): - if pass_id == 0 and batch_id == 5: - profiler.start_profiler("All") - elif pass_id == 0 and batch_id == 10: - profiler.stop_profiler("total", "/tmp/profile") - outs = exe.run(program=main_program, - feed=feeder.feed(data), - fetch_list=[out]) - - ``` - -1. 运行`python paddle/tools/timeline.py`来处理`/tmp/profile`，这个程序默认会生成一个`/tmp/timeline`文件，你也可以用命令行参数来修改这个路径，请参考[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py)。 -```python -python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline -``` - -1. 打开 chrome 浏览器，访问，用`load`按钮来加载生成的`timeline`文件。 - - -1. 结果如下图所示，可以放大来查看 timeline 的细节信息。 - - ![chrome timeline](./timeline.jpeg) - -## 分布式使用 -一般来说，分布式的训练程序都会有两种程序：pserver 和 trainer。我们提供了把 pserver 和 trainer 的 profile 日志用 timeline 来显示的方式。 - -1. trainer 打开方式与[本地使用](#local)部分的第 1 步相同 - -1. pserver 可以通过加两个环境变量打开 profile，例如： -``` -FLAGS_rpc_server_profile_period=10 FLAGS_rpc_server_profile_path=./tmp/pserver python train.py -``` - -3. 把 pserver 和 trainer 的 profile 文件生成一个 timeline 文件，例如： -``` -python /paddle/tools/timeline.py - --profile_path trainer0=local_profile_10_pass0_0,trainer1=local_profile_10_pass0_1,pserver0=./pserver_0,pserver1=./pserver_1 - --timeline_path ./dist.timeline -``` - -4. 在 chrome 中加载 dist.timeline 文件，方法和[本地使用](#local)第 4 步相同。 diff --git a/docs/guides/performance_improving/analysis_tools/timeline_en.md b/docs/guides/performance_improving/analysis_tools/timeline_en.md deleted file mode 100644 index fb51802a168..00000000000 --- a/docs/guides/performance_improving/analysis_tools/timeline_en.md +++ /dev/null @@ -1,79 +0,0 @@ -# How to use timeline tool to do profile - -## Local - -1. Add `profiler.start_profiler(...)` and `profiler.stop_profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number. - - ```python - - import numpy as np - import paddle - import paddle.fluid as fluid - from paddle.fluid import profiler - - place = fluid.CPUPlace() - - def reader(): - for i in range(100): - yield [np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32')], - - main_program = fluid.Program() - startup_program = fluid.Program() - - with fluid.program_guard(main_program, startup_program): - data_1 = fluid.layers.data(name='data_1', shape=[1, 2, 2]) - data_2 = fluid.layers.data(name='data_2', shape=[1, 1, 3]) - out = fluid.layers.fc(input=[data_1, data_2], size=2) - # ... - - feeder = fluid.DataFeeder([data_1, data_2], place) - exe = fluid.Executor(place) - exe.run(startup_program) - pass_num = 10 - - for pass_id in range(pass_num): - for batch_id, data in enumerate(reader()): - if pass_id == 0 and batch_id == 5: - profiler.start_profiler("All") - elif pass_id == 0 and batch_id == 10: - profiler.stop_profiler("total", "/tmp/profile") - outs = exe.run(program=main_program, - feed=feeder.feed(data), - fetch_list=[out]) - - ``` - -2. Run `python paddle/tools/timeline.py` to process `/tmp/profile`, it will generate another -file `/tmp/timeline` by default. You can change the path by cmd parameter, please take a look at -[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py) for details. -```python -python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline -``` - -3. Open chrome and visit , use `load` button to load the generated `timeline` file. - - - - -4. The result timeline should be like: - - ![chrome timeline](./timeline.jpeg) - -## Distributed -This tool can support distributed train programs(pserver and trainer) too. - -1. Open traniner profiler just like how to use in [local](#local). - -2. Open pserver profiler: add two environment variables, e.g.: -``` -FLAGS_rpc_server_profile_period=10 FLAGS_rpc_server_profile_path=./tmp/pserver python train.py -``` - -3. Merge pservers' and trainers' profiler file, e.g.: -``` -python /paddle/tools/timeline.py - --profile_path trainer0=local_profile_10_pass0_0,trainer1=local_profile_10_pass0_1,pserver0=./pserver_0,pserver1=./pserver_1 - --timeline_path ./dist.timeline -``` - -4. Load `dist.timeline` in chrome just like the [fourth step in Local](#local_step_4) diff --git a/docs/guides/performance_improving/device_switching.md b/docs/guides/performance_improving/device_switching.md deleted file mode 100644 index 3c15919c503..00000000000 --- a/docs/guides/performance_improving/device_switching.md +++ /dev/null @@ -1,199 +0,0 @@ -# 运行时设备切换 - -Paddle 提供了[fluid.CUDAPlace](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/CUDAPlace_cn.html)以及[fluid.CPUPlace](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/CPUPlace_cn.html)用于指定运行时的设备。这两个接口用于指定全局的设备，从 1.8 版本开始，Paddle 提供了[device_guard](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/fluid_cn/device_guard_cn.html)接口，用于指定部分 OP 的运行设备，此教程会介绍 device_guard 的使用场景，以及如何使用该接口对模型进行优化。 - -如果使用了`fluid.CUDAPlace`设置了全局的执行设备，框架将尽可能地将 OP 设置在 GPU 上执行，因此有可能会遇到显存不够的情况。`device_guard`可以用于设置 OP 的执行设备，如果将部分层设置在 CPU 上运行，就能够充分利用 CPU 大内存的优势，避免显存超出。 - -有时尽管指定了全局的执行设备为 GPU，但框架在自动分配 OP 执行设备时，可能会将部分 OP 设置在 CPU 上执行。另外，个别 OP 会将输出存储在 CPU 上。在以上的场景中，常常会发生不同设备间的数据传输，可能会影响模型的性能。使用`device_guard`可以避免模型运行中不必要的数据传输。在下面的内容中，将会详细介绍如何通过[profile](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/profiler_cn.html)工具分析数据传输开销，以及如何使用`device_guard`避免不必要的数据传输，从而提升模型性能。 - -## 如何避免显存超出 - -下面示例代码中的`embedding`层，其参数`size`包含两个元素，第一个元素为`vocab_size` (词表大小), 第二个为`emb_size`（`embedding`层维度）。实际场景中，词表可能会非常大。示例代码中，词表大小被设置为 10000000。如果在 GPU 模式下运行，该层创建的权重矩阵的大小为(10000000, 150)，仅这一层就需要 5.59G 的显存，如果词表大小继续增加，极有可能会导致显存超出。 - -```python -import paddle.fluid as fluid - -data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64') -label = fluid.layers.fill_constant(shape=[1, 150], value=0.5, dtype='float32') -emb = fluid.embedding(input=data, size=(10000000, 150), dtype='float32') -out = fluid.layers.l2_normalize(x=emb, axis=-1) - -cost = fluid.layers.square_error_cost(input=out, label=label) -avg_cost = fluid.layers.mean(cost) -sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) -sgd_optimizer.minimize(avg_cost) - -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -result = exe.run(fluid.default_main_program(), fetch_list=[avg_cost]) -``` - -`embedding`是根据`input`中的`id`信息从`embedding`矩阵中查询对应`embedding`信息，在 CPU 上进行计算，其速度也是可接受的。因此，可以参考如下代码，使用`device_guard`将`embedding`层设置在 CPU 上，以利用 CPU 内存资源。那么，除了`embedding`层，其他各层都会在 GPU 上运行。 - -```python -import paddle.fluid as fluid - -data = fluid.layers.fill_constant(shape=[1], value=128, dtype='int64') -label = fluid.layers.fill_constant(shape=[1, 150], value=0.5, dtype='float32') -with fluid.device_guard("cpu"): - emb = fluid.embedding(input=data, size=(10000000, 150), dtype='float32') -out = fluid.layers.l2_normalize(x=emb, axis=-1) - -cost = fluid.layers.square_error_cost(input=out, label=label) -avg_cost = fluid.layers.mean(cost) -sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) -sgd_optimizer.minimize(avg_cost) - -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -result = exe.run(fluid.default_main_program(), fetch_list=[avg_cost]) -``` - -在显存足够的情况下，可不必进行这样的设置。 - -## 如何减少数据传输 -### 使用 profile 工具确认是否发生了数据传输 -首先对模型的性能数据进行分析，找到发生数据传输的原因。如下列代码所示，可以利用[profile](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/profiler_cn.html)工具进行分析。 - -```python -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.fluid.profiler as profiler - -data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') -data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32') -shape = fluid.layers.shape(data2) -shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) -out = fluid.layers.crop_tensor(data1, shape=shape) -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -compiled_prog = compiler.CompiledProgram(fluid.default_main_program()) -with profiler.profiler('All', 'total') as prof: - for i in range(10): - result = exe.run(program=compiled_prog, fetch_list=[out]) -``` - -在程序运行结束后，将会自动地打印出 profile report。在下面的 profile report 中，可以看到 `GpuMemCpy Summary`中给出了 2 项数据传输的调用耗时。在 OP 执行过程中，如果输入 Tensor 所在的设备与 OP 执行的设备不同，就会发生`GpuMemcpySync`，通常我们可以直接优化的就是这一项。进一步分析，可以看到`slice`和`crop_tensor`执行中都发生了`GpuMemcpySync`。尽管我们在程序中设置了 GPU 模式运行，但是框架中有些 OP，例如 shape，会将输出结果放在 CPU 上。 - -```text --------------------------> Profiling Report <------------------------- - -Note! This Report merge all thread info into one. -Place: All -Time unit: ms -Sorted by total time in descending order in the same thread - -Total time: 26.6328 - Computation time Total: 13.3133 Ratio: 49.9884% - Framework overhead Total: 13.3195 Ratio: 50.0116% - -------------------------- GpuMemCpy Summary ------------------------- - -GpuMemcpy Calls: 30 Total: 1.47508 Ratio: 5.5386% - GpuMemcpyAsync Calls: 10 Total: 0.443514 Ratio: 1.66529% - GpuMemcpySync Calls: 20 Total: 1.03157 Ratio: 3.87331% - -------------------------- Event Summary ------------------------- - -Event Calls Total CPU Time (Ratio) GPU Time (Ratio) Min. Max. Ave. Ratio. -FastThreadedSSAGraphExecutorPrepare 10 9.16493 9.152509 (0.998645) 0.012417 (0.001355) 0.025192 8.85968 0.916493 0.344122 -shape 10 8.33057 8.330568 (1.000000) 0.000000 (0.000000) 0.030711 7.99849 0.833057 0.312793 -fill_constant 20 4.06097 4.024522 (0.991025) 0.036449 (0.008975) 0.075087 0.888959 0.203049 0.15248 -slice 10 1.78033 1.750439 (0.983212) 0.029888 (0.016788) 0.148503 0.290851 0.178033 0.0668471 - GpuMemcpySync:CPU->GPU 10 0.45524 0.446312 (0.980388) 0.008928 (0.019612) 0.039089 0.060694 0.045524 0.0170932 -crop_tensor 10 1.67658 1.620542 (0.966578) 0.056034 (0.033422) 0.143906 0.258776 0.167658 0.0629515 - GpuMemcpySync:GPU->CPU 10 0.57633 0.552906 (0.959357) 0.023424 (0.040643) 0.050657 0.076322 0.057633 0.0216398 -Fetch 10 0.919361 0.895201 (0.973721) 0.024160 (0.026279) 0.082935 0.138122 0.0919361 0.0345199 - GpuMemcpyAsync:GPU->CPU 10 0.443514 0.419354 (0.945526) 0.024160 (0.054474) 0.040639 0.059673 0.0443514 0.0166529 -ScopeBufferedMonitor::post_local_exec_scopes_process 10 0.341999 0.341999 (1.000000) 0.000000 (0.000000) 0.028436 0.057134 0.0341999 0.0128413 -eager_deletion 30 0.287236 0.287236 (1.000000) 0.000000 (0.000000) 0.005452 0.022696 0.00957453 0.010785 -ScopeBufferedMonitor::pre_local_exec_scopes_process 10 0.047864 0.047864 (1.000000) 0.000000 (0.000000) 0.003668 0.011592 0.0047864 0.00179718 -InitLocalVars 1 0.022981 0.022981 (1.000000) 0.000000 (0.000000) 0.022981 0.022981 0.022981 0.000862883 -``` -### 通过 log 查看发生数据传输的具体位置 - -以上的示例程序比较简单，我们只用看 profile report 就能知道具体是哪些算子发生了数据传输。但是当模型比较复杂时，可能需要去查看更加详细的调试信息，可以打印出运行时的 log 去确定发生数据传输的具体位置。依然以上述程序为例，执行`GLOG_vmodule=operator=3 python test_case.py`，会得到如下 log 信息，会发现发生了 2 次数据传输： - -- `shape`输出的结果在 CPU 上，在`slice`运行时，`shape`的输出被拷贝到 GPU 上 -- `slice`执行完的结果在 GPU 上，当`crop_tensor`执行时，它会被拷贝到 CPU 上。 - -```text -I0406 14:56:23.286592 17516 operator.cc:180] CUDAPlace(0) Op(shape), inputs:{Input[fill_constant_1.tmp_0:float[1, 3, 5, 5]({})]}, outputs:{Out[shape_0.tmp_0:int[4]({})]}. -I0406 14:56:23.286628 17516 eager_deletion_op_handle.cc:107] Erase variable fill_constant_1.tmp_0 on CUDAPlace(0) -I0406 14:56:23.286725 17516 operator.cc:1210] Transform Variable shape_0.tmp_0 from data_type[int]:data_layout[NCHW]:place[CPUPlace]:library_type[PLAIN] to data_type[int]:data_layout[ANY_LAYOUT]:place[CUDAPlace(0)]:library_type[PLAIN] -I0406 14:56:23.286763 17516 scope.cc:169] Create variable shape_0.tmp_0 -I0406 14:56:23.286784 17516 data_device_transform.cc:21] DeviceTransform in, src_place CPUPlace dst_place: CUDAPlace(0) -I0406 14:56:23.286867 17516 tensor_util.cu:129] TensorCopySync 4 from CPUPlace to CUDAPlace(0) -I0406 14:56:23.287099 17516 operator.cc:180] CUDAPlace(0) Op(slice), inputs:{EndsTensor[], EndsTensorList[], Input[shape_0.tmp_0:int[4]({})], StartsTensor[], StartsTensorList[]}, outputs:{Out[slice_0.tmp_0:int[4]({})]}. -I0406 14:56:23.287140 17516 eager_deletion_op_handle.cc:107] Erase variable shape_0.tmp_0 on CUDAPlace(0) -I0406 14:56:23.287220 17516 tensor_util.cu:129] TensorCopySync 4 from CUDAPlace(0) to CPUPlace -I0406 14:56:23.287473 17516 operator.cc:180] CUDAPlace(0) Op(crop_tensor), inputs:{Offsets[], OffsetsTensor[], Shape[slice_0.tmp_0:int[4]({})], ShapeTensor[], X[fill_constant_0.tmp_0:float[1, 3, 8, 8]({})]}, outputs:{Out[crop_tensor_0.tmp_0:float[1, 3, 5, 5]({})]}. -``` - -### 使用 device_guard 避免不必要的数据传输 - -在上面的例子中，`shape`输出的是一个 1-D 的 Tensor，因此对于`slice`而言计算量很小。这种情况下如果将`slice`设置在 CPU 上运行，就可以避免 2 次数据传输。修改后的程序如下： - -```python -import paddle.fluid as fluid -import paddle.fluid.compiler as compiler -import paddle.fluid.profiler as profiler - -data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') -data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32') -shape = fluid.layers.shape(data2) -with fluid.device_guard("cpu"): - shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) -out = fluid.layers.crop_tensor(data1, shape=shape) -place = fluid.CUDAPlace(0) -exe = fluid.Executor(place) -exe.run(fluid.default_startup_program()) -compiled_prog = compiler.CompiledProgram(fluid.default_main_program()) -with profiler.profiler('All', 'total') as prof: - for i in range(10): - result = exe.run(program=compiled_prog, fetch_list=[out]) -``` -再次观察 profile report 中`GpuMemCpy Summary`的内容，可以看到`GpuMemCpySync`已经被消除。在实际的模型中，若`GpuMemCpySync` 调用耗时占比较大，并且可以通过设置`device_guard`避免，那么就能够带来一定的性能提升。 - -```text --------------------------> Profiling Report <------------------------- - -Note! This Report merge all thread info into one. -Place: All -Time unit: ms -Sorted by total time in descending order in the same thread - -Total time: 14.5345 - Computation time Total: 4.47587 Ratio: 30.7948% - Framework overhead Total: 10.0586 Ratio: 69.2052% - -------------------------- GpuMemCpy Summary ------------------------- - -GpuMemcpy Calls: 10 Total: 0.457033 Ratio: 3.14447% - GpuMemcpyAsync Calls: 10 Total: 0.457033 Ratio: 3.14447% - -------------------------- Event Summary ------------------------- - -Event Calls Total CPU Time (Ratio) GPU Time (Ratio) Min. Max. Ave. Ratio. -FastThreadedSSAGraphExecutorPrepare 10 7.70113 7.689066 (0.998433) 0.012064 (0.001567) 0.032657 7.39363 0.770113 0.529852 -fill_constant 20 2.62299 2.587022 (0.986287) 0.035968 (0.013713) 0.071097 0.342082 0.13115 0.180466 -shape 10 1.93504 1.935040 (1.000000) 0.000000 (0.000000) 0.026774 1.6016 0.193504 0.133134 -Fetch 10 0.880496 0.858512 (0.975032) 0.021984 (0.024968) 0.07392 0.140896 0.0880496 0.0605797 - GpuMemcpyAsync:GPU->CPU 10 0.457033 0.435049 (0.951898) 0.021984 (0.048102) 0.037836 0.071424 0.0457033 0.0314447 -crop_tensor 10 0.705426 0.671506 (0.951916) 0.033920 (0.048084) 0.05841 0.123901 0.0705426 0.0485346 -slice 10 0.324241 0.324241 (1.000000) 0.000000 (0.000000) 0.024299 0.07213 0.0324241 0.0223084 -eager_deletion 30 0.250524 0.250524 (1.000000) 0.000000 (0.000000) 0.004171 0.016235 0.0083508 0.0172365 -ScopeBufferedMonitor::post_local_exec_scopes_process 10 0.047794 0.047794 (1.000000) 0.000000 (0.000000) 0.003344 0.014131 0.0047794 0.00328831 -InitLocalVars 1 0.034629 0.034629 (1.000000) 0.000000 (0.000000) 0.034629 0.034629 0.034629 0.00238254 -ScopeBufferedMonitor::pre_local_exec_scopes_process 10 0.032231 0.032231 (1.000000) 0.000000 (0.000000) 0.002952 0.004076 0.0032231 0.00221755 -``` - -### 总结 - -- 使用 profile 工具对模型进行分析，看是否存在 GpuMemcpySync 的调用耗时。若存在，则进一步分析发生数据传输的原因。 -- 可以通过 profile report 找到发生 GpuMemcpySync 的 OP。如果需要，可以通过打印 log，找到 GpuMemcpySync 发生的具体位置。 -- 尝试使用`device_guard`设置部分 OP 的运行设备，来减少 GpuMemcpySync 的调用。 -- 最后可以通过比较修改前后模型的 profile report，或者其他用来衡量性能的指标，确认修改后是否带来了性能提升。 diff --git a/docs/guides/performance_improving/memory_optimize.rst b/docs/guides/performance_improving/memory_optimize.rst deleted file mode 100644 index f4a892e1dd8..00000000000 --- a/docs/guides/performance_improving/memory_optimize.rst +++ /dev/null @@ -1,156 +0,0 @@ -.. _api_guide_memory_optimize: - -########### -存储分配与优化 -########### - -1. PaddlePaddle 的显存分配策略 -=========================== - -1.1. 显存自增长 AutoGrowth 策略 --------------------------- -自 1.6+的版本起，PaddlePaddle 支持显存自增长 AutoGrowth 策略，按需分配显存，且已于 1.7+版本中默认开启，方便用户在同一张 GPU 卡上同时运行多个任务。 - -由于原生的 CUDA 系统调用 :code:`cudaMalloc` 和 :code:`cudaFree` 均是同步操作，非常耗时。 -因此显存自增长 AutoGrowth 策略会缓存已分配到的显存，供后续分配使用，具体方式为： - -- 在前几次显存分配时，框架会调用 :code:`cudaMalloc` 按需分配，但释放时不会调用 :code:`cudaFree` 返回给 GPU，而是在框架内部缓存起来。 - -- 在随后的显存分配时，框架会首先检查缓存的显存中是否有合适的块，若有则从中分割出所需的显存空间返回，否则才调用 :code:`cudaMalloc` 直接从 GPU 中分配。随后的显存释放亦会缓存起来供后续分配使用。 - -因此，显存自增长 AutoGrowth 策略会在前几个 batch 训练时分配较慢（因为频繁调用 :code:`cudaMalloc` ），在随后训练过程中基本不会影响模型训练速度。 - -1.2. 显存预分配策略 ----------------- - -除了显存自增长 AutoGrowth 策略以外，PaddlePaddle 还提供了显存预分配策略。显存预分配策略是 PaddlePaddle 1.7 版本前的默认显存分配策略。 - -显存预分配策略会在第一次分配时分配很大 chunk_size 的显存块，随后的显存分配大多从预分配的显存块中切分获得。 -其中，chunk_size 由环境变量 :code:`FLAGS_fraction_of_gpu_memory_to_use` 确定，chunk_size 的计算公式为： - -.. code-block:: python - - chunk_size = FLAGS_fraction_of_gpu_memory_to_use * 单张 GPU 卡的当前可用显存值 - -:code:`FLAGS_fraction_of_gpu_memory_to_use` 的默认值为 0.92，即框架预先分配显卡 92%的当前可用显存值。 - -显存预分配策略分配显存的具体方式为： - -- 在分配 requested_size 大小的显存时， - - 若 requested_size <= chunk_size，则框架会预先分配 chunk_size 大小的显存池 chunk，并从 chunk 中分出 requested_size 大小的块返回。之后每次申请显存都会从 chunk 中分配。 - - 若 requested_size > chunk_size，则框架会直接调用 :code:`cudaMalloc` 分配 requested_size 大小的显存返回。 - -- 在释放 free_size 大小的显存时， - - 若 free_size <= chunk_size，则框架会将该显存放回预分配的 chunk 中，而不是直接返回给 CUDA。 - - 若 free_size > chunk_size，则框架会直接调用 :code:`cudaFree` 将显存返回给 CUDA。 - -若你的 GPU 卡上有其他任务占用显存，你可以适当将 :code:`FLAGS_fraction_of_gpu_memory_to_use` 减少，保证框架能预分配到合适的显存块，例如： - -.. code-block:: shell - - export FLAGS_fraction_of_gpu_memory_to_use=0.4 # 预先 40%的 GPU 显存 - -若 :code:`FLAGS_fraction_of_gpu_memory_to_use` 设为 0，则每次显存分配和释放均会调用 :code:`cudaMalloc` 和 :code:`cudaFree` ，会严重影响性能，不建议你使用。 -只有当你想测量网络的实际显存占用量时，你可以设置 :code:`FLAGS_fraction_of_gpu_memory_to_use` 为 0，观察 nvidia-smi 显示的显存占用情况。 - -1.3. 显存分配策略的选择方式 ------------------------ -自 1.6+版本起，PaddlePaddle 同时支持显存自增长 AutoGrowth 策略和显存预分配策略，并通过环境变量 :code:`FLAGS_allocator_strategy` 控制。 - -选择显存自增长 AutoGrowth 的方式为： - -.. code-block:: shell - - export FLAGS_allocator_strategy=auto_growth # 选择显存自增长 AutoGrowth 策略 - -选择显存预分配策略的方式为： - -.. code-block:: shell - - export FLAGS_allocator_strategy=naive_best_fit # 选择显存预分配策略 - -此外，自 1.7.2+版本起，PaddlePaddle 提供了环境变量 :code:`FLAGS_gpu_memory_limit_mb` ，用于控制单个任务进程可分配的最大显存，单位是 MB。默认值是 0，表示没有限制，可分配全部显存。如果设置为大于 0 的值，则会在分配的显存超过限制时报错，即使此时系统还存在空闲的显存空间。 - -2. PaddlePaddle 的存储优化策略 -=========================== - -PaddlePaddle 提供了多种通用存储优化方法，优化你的网络的存储占用（包括显存和内存)。 - -2.1. GC 策略: 存储垃圾及时回收 -------------------------- - -GC（Garbage Collection）的原理是在网络运行阶段及时释放无用变量的存储空间，达到节省存储空间的目的。GC 适用于使用 Executor，ParallelExecutor 做模型训练/预测的场合，但不适用于 C++预测库接口。 - -**GC 策略已于 1.6+版本中默认开启。** - -GC 策略由三个环境变量控制： - - -- :code:`FLAGS_eager_delete_tensor_gb` - -GC 策略的使能开关，double 类型，在<1.6 的版本中默认值为-1，在 1.6+版本中默认值为 0。GC 策略会积攒一定大小的存储垃圾后再统一释放，:code:`FLAGS_eager_delete_tensor_gb` 控制的是存储垃圾的阈值，单位是 GB。**建议用户设置** :code:`FLAGS_eager_delete_tensor_gb=0` 。 - -若 :code:`FLAGS_eager_delete_tensor_gb=0` ，则一旦有存储垃圾则马上回收，最为节省存储空间。 - -若 :code:`FLAGS_eager_delete_tensor_gb=1` ，则存储垃圾积攒到 1G 后才触发回收。 - -若 :code:`FLAGS_eager_delete_tensor_gb<0` ，则 GC 策略关闭。 - - -- :code:`FLAGS_memory_fraction_of_eager_deletion` - -GC 策略的调节 flag，double 类型，默认值为 1，范围为[0,1]，仅适用于使用 ParallelExecutor 的场合。 -GC 内部会根据变量占用的存储空间大小，对变量进行降序排列，且仅回收前 :code:`FLAGS_memory_fraction_of_eager_deletion` 大的变量的存储空间。**建议用户维持默认值**，即 :code:`FLAGS_memory_fraction_of_eager_deletion=1` 。 - -若 :code:`FLAGS_memory_fraction_of_eager_deletion=0.6` ，则表示仅回收存储占用 60%大的变量的存储空间。 - -若 :code:`FLAGS_memory_fraction_of_eager_deletion=0` ，则表示不回收任何变量的存储空间，GC 策略关闭。 - -若 :code:`FLAGS_memory_fraction_of_eager_deletion=1` ，则表示回收所有变量的存储空间。 - - -- :code:`FLAGS_fast_eager_deletion_mode` - -快速 GC 策略的开关，bool 类型，默认值为 True，表示使用快速 GC 策略。快速 GC 策略会不等待 CUDA Kernel 结束直接释放显存。**建议用户维持默认值**，即 :code:`FLAGS_fast_eager_deletion_mode=True` 。 - - -2.2. Inplace 策略: Op 内部的输出复用输入 ----------------------------------- - -Inplace 策略的原理是 Op 的输出复用 Op 输入的存储空间。例如，reshape 操作的输出和输入可复用同一片存储空间。 - -Inplace 策略适用于使用 ParallelExecutor 的场合，通过 :code:`BuildStrategy` 设置。此策略不支持使用 Executor+Program 做单卡训练、使用 C++预测库接口等场合。 - -**Inplace 策略已于 1.6+版本中默认开启。** - -具体方式为: - -.. code-block:: python - - build_strategy = fluid.BuildStrategy() - build_strategy.enable_inplace = True # 开启 Inplace 策略 - - compiled_program = fluid.CompiledProgram(train_program, build_strategy=build_strategy) - - -在<1.6 的版本中，由于设计上的一些问题，在开启 Inplace 策略后，必须保证后续 exe.run 中 fetch_list 的变量是 persistable 的，即假如你后续需要 fetch 的变量为 loss 和 acc，则必须设置： - -.. code-block:: python - - loss.persistable = True - acc.persistable = True - - -**在 1.6+的版本中，无需设置 fetch 变量为 persistable。** - - -3. 存储优化 Best Practice -======================= - -我们推荐你的最佳存储优化策略为： - -- 开启 GC 策略：设置 :code:`FLAGS_eager_delete_tensor_gb=0` 。 - -- 开启 Inplace 策略：设置 :code:`build_strategy.enable_inplace = True` ，并在<1.6 版本中设置 fetch_list 中的 :code:`var.persistable = True` 。 - -**在 1.6+的版本中，上述最佳策略均已默认打开，无需手动配置，亦无需设置 fetch_list 变量为 persistable。** diff --git a/docs/guides/performance_improving/memory_optimize_en.rst b/docs/guides/performance_improving/memory_optimize_en.rst deleted file mode 100644 index 0a2eceb9bca..00000000000 --- a/docs/guides/performance_improving/memory_optimize_en.rst +++ /dev/null @@ -1,176 +0,0 @@ -.. _api_guide_memory_optimize_en: - -########### -Memory Allocation and Optimization -########### - -1. Memory Allocation Strategy -=========================== - -1.1. AutoGrowth Strategy --------------------------- - -Since version 1.6+, PaddlePaddle supports the AutoGrowth strategy, which allocates memory on demand. -AutoGrowth strategy has been enabled by default in version 1.7+, making it convenient for users to -run multiple tasks on the same GPU card at the same time. - -Because the native CUDA system calls :code:`cudaMalloc` and :code:`cudaFree` are synchronous operations, -which are very time-consuming, the AutoGrowth strategy will cache the allocated memory for subsequent allocation. -The specific methods are as follows: - -- In the first few memory allocations, PaddlePaddle framework will call :code:`cudaMalloc` and allocate memory on demand. When releasing the allocated memory, it will not call :code:`cudaFree` to return the memory to GPU, but cache the memory inside the framework. - -- In the subsequent allocations, PaddlePaddle framework will first check if there is a fit block (block size larger than the required memory size) in the cached memory. If there is, it will split the required memory from the fit block and return. Otherwise, it will call :code:`cudaMalloc` to allocate memory from GPU. The allocated memory are also cached when being released for subsequent allocation. - -Therefore, the AutoGrowth strategy may slow the speed in the first few batches of model training, -but will not affect the speed in the subsequent training process. - -1.2. Pre-Allocation Strategy ----------------- - -In addition to the AutoGrowth strategy, paddlepaddle also provides a Pre-Allocation strategy, -which is the default memory allocation strategy before paddlepaddle 1.7. - -The Pre-Allocation strategy allocates a large size chunk at the first allocation, and the subsequent memory allocation is mostly obtained from the pre allocated memory chunk. -Among them, the chunk size is determined by the environment variable :code:`FLAGS_fraction_of_gpu_memory_to_use`, and the calculation formula of chunk size is: - -.. code-block:: python - - chunk_size = FLAGS_fraction_of_gpu_memory_to_use * number of current available memory of a single GPU card - -The default value of :code:`FLAGS_fraction_of_gpu_memory_to_use` is 0.92, that is, the framework will pre allocates -92% of the currently available memory of the GPU card. - -The specific way of Pre-Allocation strategy to allocate GPU memory is: - -- When allocating memory of requested_size, - - If requested_size <= chunk_size, the framework will first allocate a memory chunk of chunk_size, then split a block of requested_size and return the block. Every subsequent memory allocation will be performed on the chunk. - - If requested_size > chunk_size, the framework will call :code:`cudaMalloc` to allocate memory block of requested_size and return. - -- When freeing memory of requested_size, - - If free_size <= chunk_size, the framework will put the memory block back into the pre-allocated chunk, instead of returning back to GPU. - - If free_size > chunk_size, the framework will call :code:`cudaFree` and return the memory back to GPU. - -If there are other tasks on your GPU card that occupy the memory, you can appropriately decrease :code:`FLAGS_fraction_of_gpu_memory_to_use` -to ensure that the framework can pre-allocate the memory block of appropriate size, for example - -.. code-block:: shell - - export FLAGS_fraction_of_gpu_memory_to_use=0.4 # Pre-allocate 40% memory of a single GPU card - -If :code:`FLAGS_fraction_of_gpu_memory_to_use` is set to 0, the framework will call :code:`cudaMalloc` and :code:`cudaFree` every time the memory is allocated and released, which will seriously affect the performance and is not recommended. Only when you want to measure the actual memory usage of the network, you could set :code:`FLAGS_fraction_of_gpu_memory_to_use` to 0, and observe the memory usage of command nvidia-smi display. - -1.3. Configuration of memory allocation strategy ------------------------ -Since version 1.6+, PaddlePaddle supports both the AutoGrowth strategy and the Pre-Allocation Strategy, and control the strategy used in framework by -the environment variable :code:`FLAGS_allocator_strategy`. - -Use AutoGrowth strategy: - -.. code-block:: shell - - export FLAGS_allocator_strategy=auto_growth # Use AutoGrowth strategy - -Use Pre-Allocation strategy: - -.. code-block:: shell - - export FLAGS_allocator_strategy=naive_best_fit # Use Pre-Allocation strategy - -Plus, since version 1.7.2+, PaddlePaddle provides an environment variable :code:`FLAGS_gpu_memory_limit_mb`, which controls the maximum gpu memory limit that the process can allocate. -If it is equal to 0, there would be no limit and all gpu memory would be available to the process. If it is larger than 0, the process would raise out of memory error if the allocated -memory exceeds the limit even though there is available memory on the gpu card. The unit is MB and default value is 0. - -2. Memory Optimization Strategy -=========================== - -Paddlepaddle provides several general memory optimization methods to optimize the memory usage of your network (including general memory and GPU memory). - -2.1. GC Strategy: memory garbage eager collection -------------------------- - -The principle of GC（Garbage Collection）is to release the memory space of useless variables eagerly during network running, -in order to save memory space. GC is suitable for training and inference using Executor or ParallelExecutor, but it is not suitable for C++ inference library. - -**Since version 1.6+, GC Strategy is enabled by default.** - -GC Strategy is controlled by 3 environment variable: - - -- :code:`FLAGS_eager_delete_tensor_gb` - -Variable to enable GC, its data type is double. The default value is -1 in PaddlePaddle with version < 1.6, -and is 0 in PaddlePaddle with version >= 1.6. GC Strategy will cache a certain amount of memory garbage and release it uniformly. -:code:`FLAGS_eager_delete_tensor_gb` means the threshold of cached memory garbage, the unit of which is GB. **It is recommended to set** :code:`FLAGS_eager_delete_tensor_gb=0`. - -If :code:`FLAGS_eager_delete_tensor_gb=0`, once there is memory garbage, it will be collected immediately to save memory. - -If :code:`FLAGS_eager_delete_tensor_gb=1`, the memory garbage is collected when the cached amount of garbage reaches 1GB. - -If :code:`FLAGS_eager_delete_tensor_gb<0`, GC Strategy is disabled. - - -- :code:`FLAGS_memory_fraction_of_eager_deletion` - -Variable to control GC Strategy, its data type is double. The default value is 1, range [0,1]. It is only suitable for ParallelExecutor. -GC will sort the variables in descending order according to the memory space occupied by the variables, -and only collect the memory space of top :code:`FLAGS_memory_fraction_of_eager_deletion` variables. -**It is recommended to remain default value**, that is :code:`FLAGS_memory_fraction_of_eager_deletion=1`. - -If :code:`FLAGS_memory_fraction_of_eager_deletion=0.6`, top 60% variables will be collected. - -If :code:`FLAGS_memory_fraction_of_eager_deletion=0`, no variable will be collected, GC Strategy is disabled. - -If :code:`FLAGS_memory_fraction_of_eager_deletion=1`, all variables will be collected. - - -- :code:`FLAGS_fast_eager_deletion_mode` - -Variable to enable fast GC Strategy, its type is bool. The default value is True, which means use fast GC Strategy. -Fast GC Strategy will collect the memory garbage immediately instead of waiting for CUDA Kernel finish. **It is recommended to remain default value**, that is :code:`FLAGS_fast_eager_deletion_mode=True`. - - -2.2. Inplace Strategy: output reuses input inside operator ----------------------------------- - -The principle of Inplace strategy is that the output of some operators can reuses the memory space of input. -For example, the output and input of operator :code:`reshape` can reuse the same memory space. - -Inplace Strategy is suitable for ParallelExecutor, which can be set through :code:`BuildStrategy`. -The Strategy is not suitable for Executor+Program or C++ inference library. - -**Since version 1.6+, Inplace Strategy is enabled by default.** - -The specific way of Inplace strategy is: - -.. code-block:: python - - build_strategy = fluid.BuildStrategy() - build_strategy.enable_inplace = True # Enable Inplace Strategy - - compiled_program = fluid.CompiledProgram(train_program, build_strategy=build_strategy) - - -In PaddlePaddle with version < 1.6, due to of some design problems, when the Inplace Strategy is enabled, -the variable in fetch_list in the subsequent :code:`exe.run` must be persistent. -That is, if you the variables you want to fetch are loss and acc, you must set: - -.. code-block:: python - - loss.persistable = True - acc.persistable = True - - -**Since version 1.6+, setting variables in fetch_list to persistable is not needed.** - - -3. Memory Optimization Best Practice -======================= - -We recommend the best memory optimization strategy as: - -- Enable GC strategy:set :code:`FLAGS_eager_delete_tensor_gb=0`. - -- Enable Inplace strategy:set :code:`build_strategy.enable_inplace = True`, and set variables in fetch_list to persistable using :code:`var.persistable = True` when the version of PaddlePaddle < 1.6. - -**Since version 1.6+, the above optimal strategy have been enabled by default and setting variables in fetch_list to persistable is not needed.** diff --git a/docs/guides/performance_improving/paddle_tensorrt_infer.md b/docs/guides/performance_improving/paddle_tensorrt_infer.md deleted file mode 100644 index 2890eceb4ab..00000000000 --- a/docs/guides/performance_improving/paddle_tensorrt_infer.md +++ /dev/null @@ -1,209 +0,0 @@ -# 使用 Paddle-TensorRT 库预测 - -NVIDIA TensorRT 是一个高性能的深度学习预测库，可为深度学习推理应用程序提供低延迟和高吞吐量。PaddlePaddle 采用子图的形式对 TensorRT 进行了集成，即我们可以使用该模块来提升 Paddle 模型的预测性能。该模块依旧在持续开发中，目前支持的模型如下表所示： - -|分类模型|检测模型|分割模型| -|---|---|---| -|mobilenetv1|yolov3|ICNET| -|resnet50|SSD|| -|vgg16|mask-rcnn|| -|resnext|faster-rcnn|| -|AlexNet|cascade-rcnn|| -|Se-ResNext|retinanet|| -|GoogLeNet|mobilenet-SSD|| -|DPN||| - -在这篇文档中，我们将会对 Paddle-TensorRT 库的获取、使用和原理进行介绍。 - -**Note:** - -1. 从源码编译时，TensorRT 预测库目前仅支持使用 GPU 编译，且需要设置编译选项 TENSORRT_ROOT 为 TensorRT 所在的路径。 -2. Windows 支持需要 TensorRT 版本 5.0 以上。 -3. Paddle-TRT 目前仅支持固定输入 shape。 -4. 下载安装 TensorRT 后，需要手动在`NvInfer.h`文件中为`class IPluginFactory`和`class IGpuAllocator`分别添加虚析构函数： - ``` c++ - virtual ~IPluginFactory() {}; - virtual ~IGpuAllocator() {}; - ``` - -## 内容 -- [Paddle-TRT 使用介绍](#Paddle-TRT 使用介绍) -- [Paddle-TRT 样例编译测试](#Paddle-TRT 样例编译测试) -- [Paddle-TRT INT8 使用](#Paddle-TRT_INT8 使用) -- [Paddle-TRT 子图运行原理](#Paddle-TRT 子图运行原理) -- [Paddle-TRT 性能测试](#Paddle-TRT 性能测试) - -## Paddle-TRT 使用介绍 - -在使用 AnalysisPredictor 时，我们通过配置 AnalysisConfig 中的接口 - -``` c++ -config->EnableTensorRtEngine(1 << 20 /* workspace_size*/, - batch_size /* max_batch_size*/, - 3 /* min_subgraph_size*/, - AnalysisConfig::Precision::kFloat32 /* precision*/, - false /* use_static*/, - false /* use_calib_mode*/); -``` -的方式来指定使用 Paddle-TRT 子图方式来运行。 -该接口中的参数的详细介绍如下： - -- **`workspace_size`**，类型：int，默认值为 1 << 20。指定 TensorRT 使用的工作空间大小，TensorRT 会在该大小限制下筛选合适的 kernel 执行预测运算。 -- **`max_batch_size`**，类型：int，默认值为 1。需要提前设置最大的 batch 大小，运行时 batch 大小不得超过此限定值。 -- **`min_subgraph_size`**，类型：int，默认值为 3。Paddle-TRT 是以子图的形式运行，为了避免性能损失，当子图内部节点个数大于`min_subgraph_size`的时候，才会使用 Paddle-TRT 运行。 -- **`precision`**，类型：`enum class Precision {kFloat32 = 0, kHalf, kInt8,};`, 默认值为`AnalysisConfig::Precision::kFloat32`。指定使用 TRT 的精度，支持 FP32（kFloat32），FP16（kHalf），Int8（kInt8）。若需要使用 Paddle-TRT int8 离线量化校准，需设定`precision`为 `AnalysisConfig::Precision::kInt8`, 且设置`use_calib_mode` 为 true。 -- **`use_static`**，类型：bool, 默认值为 false。如果指定为 true，在初次运行程序的时候会将 TRT 的优化信息进行序列化到磁盘上，下次运行时直接加载优化的序列化信息而不需要重新生成。 -- **`use_calib_mode`**，类型：bool, 默认值为 false。若要运行 Paddle-TRT int8 离线量化校准，需要将此选项设置为 true。 - -**Note：** Paddle-TRT 目前只支持固定 shape 的输入，不支持变化 shape 的输入。 - -## Paddle-TRT 样例编译测试 - -1. 下载或编译带有 TensorRT 的 paddle 预测库，参考[安装与编译 C++预测库](../../inference_deployment/inference/build_and_install_lib_cn.html)。 -2. 从[NVIDIA 官网](https://developer.nvidia.com/nvidia-tensorrt-download)下载对应本地环境中 cuda 和 cudnn 版本的 TensorRT，需要登陆 NVIDIA 开发者账号。 -3. 下载[预测样例](https://paddle-inference-dist.bj.bcebos.com/tensorrt_test/paddle_inference_sample_v1.7.tar.gz)并解压，进入`sample/paddle-TRT`目录下。 - - `paddle-TRT` 文件夹目录结构如下： - - ``` - paddle-TRT - ├── CMakeLists.txt - ├── mobilenet_test.cc - ├── fluid_generate_calib_test.cc - ├── fluid_int8_test.cc - ├── mobilenetv1 - │ ├── model - │ └── params - ├── run.sh - └── run_impl.sh - ``` - - - `mobilenet_test.cc` 为使用 paddle-TRT 预测的 C++源文件 - - `fluid_generate_calib_test.cc` 为使用 TRT int8 离线量化校准的 C++源文件 - - `fluid_int8_test.cc` 为使用 TRT 执行 int8 预测的 C++源文件 - - `mobilenetv1` 为模型文件夹 - - `run.sh` 为预测运行脚本文件 - - 在这里假设样例所在的目录为 `SAMPLE_BASE_DIR/sample/paddle-TRT` - -4. 配置编译与运行脚本 - - 编译运行预测样例之前，需要根据运行环境配置编译与运行脚本`run.sh`。`run.sh`的选项与路径配置的部分如下： - - ```shell - # 设置是否开启 MKL、GPU、TensorRT，如果要使用 TensorRT，必须打开 GPU - WITH_MKL=ON - WITH_GPU=ON - USE_TENSORRT=ON - - # 按照运行环境设置预测库路径、CUDA 库路径、CUDNN 库路径、TensorRT 路径、模型路径 - LIB_DIR=YOUR_LIB_DIR - CUDA_LIB_DIR=YOUR_CUDA_LIB_DIR - CUDNN_LIB_DIR=YOUR_CUDNN_LIB_DIR - TENSORRT_ROOT_DIR=YOUR_TENSORRT_ROOT_DIR - MODEL_DIR=YOUR_MODEL_DIR - ``` - - 按照实际运行环境配置`run.sh`中的选项开关和所需 lib 路径。 - -5. 编译与运行样例 - - -## Paddle-TRT INT8 使用 - -1. Paddle-TRT INT8 简介 - 神经网络的参数在一定程度上是冗余的，在很多任务上，我们可以在保证模型精度的前提下，将 Float32 的模型转换成 Int8 的模型。目前，Paddle-TRT 支持离线将预训练好的 Float32 模型转换成 Int8 的模型，具体的流程如下： - - 1) **生成校准表**（Calibration table）：我们准备 500 张左右的真实输入数据，并将数据输入到模型中去，Paddle-TRT 会统计模型中每个 op 输入和输出值的范围信息，并将其记录到校准表中，这些信息有效减少了模型转换时的信息损失。 - - 2) 生成校准表后，再次运行模型，**Paddle-TRT 会自动加载校准表**，并进行 INT8 模式下的预测。 - -2. 编译测试 INT8 样例 - 将`run.sh`文件中的`mobilenet_test`改为`fluid_generate_calib_test`，运行 - - ``` shell - sh run.sh - ``` - - 即可执行生成校准表样例，在该样例中，我们随机生成了 500 个输入来模拟这一过程，在实际业务中，建议大家使用真实样例。运行结束后，在 `SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/_opt_cache` 模型目录下会多出一个名字为 trt_calib_*的文件，即校准表。 - - 生成校准表后，将带校准表的模型文件拷贝到特定地址 - - ``` shell - cp -rf SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/ SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib - ``` - - 将`run.sh`文件中的`fluid_generate_calib_test`改为`fluid_int8_test`，将模型路径改为`SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib`，运行 - - ``` shell - sh run.sh - ``` - - 即可执行 int8 预测样例。 - -## Paddle-TRT 子图运行原理 - - PaddlePaddle 采用子图的形式对 TensorRT 进行集成，当模型加载后，神经网络可以表示为由变量和运算节点组成的计算图。Paddle TensorRT 实现的功能是对整个图进行扫描，发现图中可以使用 TensorRT 优化的子图，并使用 TensorRT 节点替换它们。在模型的推断期间，如果遇到 TensorRT 节点，Paddle 会调用 TensorRT 库对该节点进行优化，其他的节点调用 Paddle 的原生实现。TensorRT 在推断期间能够进行 Op 的横向和纵向融合，过滤掉冗余的 Op，并对特定平台下的特定的 Op 选择合适的 kernel 等进行优化，能够加快模型的预测速度。 - -下图使用一个简单的模型展示了这个过程： - -**原始网络** -

- -

- -**转换的网络** -

- -

- - - 我们可以在原始模型网络中看到，绿色节点表示可以被 TensorRT 支持的节点，红色节点表示网络中的变量，黄色表示 Paddle 只能被 Paddle 原生实现执行的节点。那些在原始网络中的绿色节点被提取出来汇集成子图，并由一个 TensorRT 节点代替，成为转换后网络中的`block-25` 节点。在网络运行过程中，如果遇到该节点，Paddle 将调用 TensorRT 库来对其执行。 - -## Paddle-TRT 性能测试 - -### 测试环境 -- CPU:Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU:Tesla P4 -- TensorRT4.0, CUDA8.0, CUDNNV7 -- 测试模型 ResNet50，MobileNet，ResNet101, Inception V3. - -### 测试对象 -**PaddlePaddle, PyTorch, TensorFlow** - -- 在测试中，PaddlePaddle 使用子图优化的方式集成了 TensorRT, 模型[地址](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification/models)。 -- PyTorch 使用了原生的实现, 模型[地址 1](https://github.com/pytorch/vision/tree/master/torchvision/models)、[地址 2](https://github.com/marvis/pytorch-mobilenet)。 -- 对 TensorFlow 测试包括了对 TF 的原生的测试，和对 TF—TRT 的测试，**对 TF—TRT 的测试并没有达到预期的效果，后期会对其进行补充**， 模型[地址](https://github.com/tensorflow/models)。 - - -#### ResNet50 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|4.64117 |16.3|10.878| -|5|6.90622| 22.9 |20.62| -|10|7.9758 |40.6|34.36| - -#### MobileNet - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1| 1.7541 | 7.8 |2.72| -|5| 3.04666 | 7.8 |3.19| -|10|4.19478 | 14.47 |4.25| - -#### ResNet101 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|8.95767| 22.48 |18.78| -|5|12.9811 | 33.88 |34.84| -|10|14.1463| 61.97 |57.94| - - -#### Inception v3 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|15.1613 | 24.2 |19.1| -|5|18.5373 | 34.8 |27.2| -|10|19.2781| 54.8 |36.7| diff --git a/docs/guides/performance_improving/paddle_tensorrt_infer_en.md b/docs/guides/performance_improving/paddle_tensorrt_infer_en.md deleted file mode 100644 index 0acc384ab2a..00000000000 --- a/docs/guides/performance_improving/paddle_tensorrt_infer_en.md +++ /dev/null @@ -1,200 +0,0 @@ -# Use Paddle-TensorRT Library for inference - -NVIDIA TensorRT is a is a platform for high-performance deep learning inference. It delivers low latency and high throughput for deep learning inference application. -Subgraph is used in PaddlePaddle to preliminarily integrate TensorRT, which enables TensorRT module to enhance inference performance of paddle models. The module is still under development. Currently supported models are as following: - -|classification|detection|segmentation| -|---|---|---| -|mobilenetv1|yolov3|ICNET| -|resnet50|SSD|| -|vgg16|mask-rcnn|| -|resnext|faster-rcnn|| -|AlexNet|cascade-rcnn|| -|Se-ResNext|retinanet|| -|GoogLeNet|mobilenet-SSD|| -|DPN||| - -We will introduce the obtaining, usage and theory of Paddle-TensorRT library in this documentation. - -**Note:** - -1. When compiling from source, TensorRT library currently only supports GPU compilation, and you need to set the compilation option TensorRT_ROOT to the path where tensorrt is located. -2. Windows support requires TensorRT version 5.0 or higher. -3. Paddle-TRT currently only supports fixed input shape. -4. After downloading and installing tensorrt, you need to manually add virtual destructors for `class IPluginFactory` and `class IGpuAllocator` in the `NvInfer.h` file: - ``` c++ - virtual ~IPluginFactory() {}; - virtual ~IGpuAllocator() {}; - ``` - -## Paddle-TRT interface usage - -When using AnalysisPredictor, we enable Paddle-TRT by setting - -``` c++ -config->EnableTensorRtEngine(1 << 20 /* workspace_size*/, - batch_size /* max_batch_size*/, - 3 /* min_subgraph_size*/, - AnalysisConfig::Precision::kFloat32 /* precision*/, - false /* use_static*/, - false /* use_calib_mode*/); -``` -The details of this interface is as following: - -- **`workspace_size`**: type:int, default is 1 << 20. Sets the max workspace size of TRT. TensorRT will choose kernels under this constraint. -- **`max_batch_size`**: type:int, default is 1. Sets the max batch size. Batch sizes during runtime cannot exceed this value. -- **`min_subgraph_size`**: type:int, default is 3. Subgraph is used to integrate TensorRT in PaddlePaddle. To avoid low performance, Paddle-TRT is only enabled when th number of nodes in th subgraph is more than `min_subgraph_size`. -- **`precision`**: type:`enum class Precision {kFloat32 = 0, kHalf, kInt8,};`, default is `AnalysisConfig::Precision::kFloat32`. Sets the precision of TRT, supporting FP32(kFloat32), FP16(kHalf), Int8(kInt8). Using Paddle-TRT int8 calibration requires setting `precision` to `AnalysisConfig::Precision::kInt8`, and `use_calib_mode` to true. -- **`use_static`**: type:bool, default is false. If set to true, Paddle-TRT will serialize optimization information to disk, to deserialize next time without optimizing again. -- **`use_calib_mode`**: type:bool, default is false. Using Paddle-TRT int8 calibration requires setting this option to true. - -**Note：** Paddle-TRT currently only supports fixed input shape. - -## Paddle-TRT example compiling test - -1. Download or compile Paddle Inference with TensorRT support, refer to [Install and Compile C++ Inference Library](../../inference_deployment/inference/build_and_install_lib_en.html). -2. Download NVIDIA TensorRT(with consistent version of cuda and cudnn in local environment) from [NVIDIA TensorRT](https://developer.nvidia.com/nvidia-tensorrt-download) with an NVIDIA developer account. -3. Download [Paddle Inference sample](https://paddle-inference-dist.bj.bcebos.com/tensorrt_test/paddle_inference_sample_v1.7.tar.gz) and uncompress, and enter `sample/paddle-TRT` directory. - - `paddle-TRT` directory structure is as following: - - ``` - paddle-TRT - ├── CMakeLists.txt - ├── mobilenet_test.cc - ├── fluid_generate_calib_test.cc - ├── fluid_int8_test.cc - ├── mobilenetv1 - │ ├── model - │ └── params - ├── run.sh - └── run_impl.sh - ``` - - - `mobilenet_test.cc` is the c++ source code of inference using Paddle-TRT - - `fluid_generate_calib_test.cc` is the c++ source code of inference using Paddle-TRT int8 calibration to generate calibration table - - `fluid_int8_test.cc` is the c++ source code of inference using Paddle-TRT int8 - - `mobilenetv1` is the model dir - - `run.sh` is the script for running inference - - Here we assume that the current directory is `SAMPLE_BASE_DIR/sample/paddle-TRT`. - - ``` shell - # set whether to enable MKL, GPU or TensorRT. Enabling TensorRT requires WITH_GPU being ON - WITH_MKL=ON - WITH_GPU=OFF - USE_TENSORRT=OFF - - # set path to CUDA lib dir, CUDNN lib dir, TensorRT root dir and model dir - LIB_DIR=YOUR_LIB_DIR - CUDA_LIB_DIR=YOUR_CUDA_LIB_DIR - CUDNN_LIB_DIR=YOUR_CUDNN_LIB_DIR - TENSORRT_ROOT_DIR=YOUR_TENSORRT_ROOT_DIR - MODEL_DIR=YOUR_MODEL_DIR - ``` - - Please configure `run.sh` depending on your environment. - -4. Build and run the sample. - - ``` shell - sh run.sh - ``` - -## Paddle-TRT INT8 usage - -1. Paddle-TRT INT8 introduction - The parameters of the neural network are redundant to some extent. In many tasks, we can turn the Float32 model into Int8 model on the premise of precision. At present, Paddle-TRT supports to turn the trained Float32 model into Int8 model off line. The specific processes are as follows: - - 1）**Create the calibration table**. We prepare about 500 real input data, and input the data to the model. Paddle-TRT will count the range information of each op input and output value in the model, and record in the calibration table. The information can reduce the information loss during model transformation. - - 2）After creating the calibration table, run the model again, **Paddle-TRT will load the calibration table automatically**, and conduct the inference in the INT8 mode. - -2. compile and test the INT8 example - - change the `mobilenet_test` in `run.sh` to `fluid_generate_calib_test` and run - - ``` shell - sh run.sh - ``` - - We generate 500 input data to simulate the process, and it's suggested that you use real example for experiment. After the running period, there will be a new file named trt_calib_* under the `SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/_opt_cache` model directory, which is the calibration table. - - Then copy the model dir with calibration infomation to path - - ``` shell - cp -rf SAMPLE_BASE_DIR/sample/paddle-TRT/build/mobilenetv1/ SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib - ``` - - change `fluid_generate_calib_test` in `run.sh` to `fluid_int8_test`, and change model dir path to `SAMPLE_BASE_DIR/sample/paddle-TRT/mobilenetv1_calib` and run - - ``` shell - sh run.sh - ``` - -## Paddle-TRT subgraph operation principle - - Subgraph is used to integrate TensorRT in PaddlePaddle. After model is loaded, neural network can be represented as a computing graph composed of variables and computing nodes. Functions Paddle TensorRT implements are to scan the whole picture, discover subgraphs that can be optimized with TensorRT and replace them with TensorRT nodes. During the inference of model, Paddle will call TensorRT library to optimize TensorRT nodes and call native library of Paddle to optimize other nodes. During the inference, TensorRT can integrate Op horizonally and vertically to filter redundant Ops and is able to choose appropriate kernel for specific Op in specific platform to speed up the inference of model. - - -A simple model expresses the process : - -**Original Network** -

- -

- -**Transformed Network** -

- -

- - We can see in the Original Network that the green nodes represent nodes supported by TensorRT, the red nodes represent variables in network and yellow nodes represent nodes which can only be operated by native functions in Paddle. Green nodes in original network are extracted to compose subgraph which is replaced by a single TensorRT node to be transformed into `block-25` node in network. When such nodes are encountered during the runtime, TensorRT library will be called to execute them. - -## Paddle-TRT benchmark - -### Test Environment -- CPU:Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz GPU:Tesla P4 -- TensorRT 4.0, CUDA 8.0, CUDNN V7 -- models: ResNet50，MobileNet，ResNet101, Inception V3. - -### Test set -**PaddlePaddle, PyTorch, TensorFlow** - -- PaddlePaddle integrates TensorRT with subgraph, model[link](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification/models)。 -- PyTorch uses original kernels, model[link1](https://github.com/pytorch/vision/tree/master/torchvision/models), [link2](https://github.com/marvis/pytorch-mobilenet)。 -- We tested TF original and TF-TRT**对 TF—TRT 的测试并没有达到预期的效果，后期会对其进行补充**, model[link](https://github.com/tensorflow/models)。 - - -#### ResNet50 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|4.64117 |16.3|10.878| -|5|6.90622| 22.9 |20.62| -|10|7.9758 |40.6|34.36| - -#### MobileNet - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1| 1.7541 | 7.8 |2.72| -|5| 3.04666 | 7.8 |3.19| -|10|4.19478 | 14.47 |4.25| - -#### ResNet101 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|8.95767| 22.48 |18.78| -|5|12.9811 | 33.88 |34.84| -|10|14.1463| 61.97 |57.94| - - -#### Inception v3 - -|batch_size|PaddlePaddle(ms)|PyTorch(ms)|TensorFlow(ms)| -|---|---|---|---| -|1|15.1613 | 24.2 |19.1| -|5|18.5373 | 34.8 |27.2| -|10|19.2781| 54.8 |36.7| diff --git a/docs/guides/performance_improving/quantization.md b/docs/guides/performance_improving/quantization.md deleted file mode 100644 index c07c0b00d6f..00000000000 --- a/docs/guides/performance_improving/quantization.md +++ /dev/null @@ -1,169 +0,0 @@ -# 飞桨模型量化 - -深度学习技术飞速发展，在很多任务和领域都超越传统方法。但是，深度学习模型通常需要较大的存储空间和计算量，给部署应用带来了不小挑战。 - -模型量化作为一种常见的模型压缩方法，使用整数替代浮点数进行存储和计算，可以减少模型存储空间、加快推理速度、降低计算内存，助力深度学习应用的落地。 - -飞桨提供了模型量化全流程解决方案，首先使用 PaddleSlim 产出量化模型，然后使用 Paddle Inference 和 Paddle Lite 部署量化模型。 - -
- missing -
图 1. 飞桨模型量化全流程解决方案
-
- -## 产出量化模型 - -飞桨模型量化全流程解决方案中，PaddleSlim 负责产出量化模型。 - -PaddleSlim 支持三种模型量化方法：动态离线量化方法、静态离线量化方法和量化训练方法。这三种量化方法的特点如下图。 - -
- missing -
图 2. 量化方法概述
-
- -动态离线量化方法不需要使用样本数据，也不会对模型进行训练。在模型产出阶段，动态离线量化方法将模型权重从浮点数量化成整数。在模型部署阶段，将权重从整数反量化成浮点数，使用浮点数运算进行预测推理。这种方式主要减少模型存储空间，对权重读取费时的模型有一定加速作用，对模型精度影响较小。 - -静态离线量化方法要求有少量无标签样本数据，需要执行模型的前向计算，不会对模型进行训练。在模型产出阶段，静态离线量化方法使用样本数据执行模型的前向计算，同时对量化 OP 的输入输出进行采样，然后计算量化信息。在模型部署阶段，使用计算好的量化信息对输入进行量化，基于整数运算进行预测推理。静态离线量化方法可以减少模型存储空间、加快模型推理速度、降低计算内存，同时量化模型只存在较小的精度损失。 - -量化训练方法要求有大量有标签样本数据，需要对模型进行较长时间的训练。在模型产出阶段，量化训练方法使用模拟量化的思想，在模型训练过程中会更新权重，实现拟合、减少量化误差的目的。在模型部署阶段，量化训练方法和静态离线量化方法一致，采用相同的预测推理方式，在存储空间、推理速度、计算内存三方面实现相同的收益。更重要的是，量化训练方法对模型精度只有极小的影响。 - - -根据使用条件和压缩目的，大家可以参考下图选用不同的模型量化方法产出量化模型。 - -
- missing -
图 3. 选择量化方法
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- -产出量化模型的使用方法、Demo 和 API，请参考[PaddleSlim 文档](https://paddleslim.readthedocs.io/zh_CN/latest/index.html)。 - -## 部署量化模型 - -飞桨模型量化全流程解决方案中，Paddle Inference 负责在服务器端（X86 CPU 和 Nvidia GPU）部署量化模型，Paddle Lite 负责在移动端（ARM CPU）上部署量化模型。 - -X86 CPU 和 Nvidia GPU 上支持部署 PaddleSlim 静态离线量化方法和量化训练方法产出的量化模型。 -ARM CPU 上支持部署 PaddleSlim 动态离线量化方法、静态离线量化方法和量化训练方法产出的量化模型。 - -因为动态离线量化方法产出的量化模型主要是为了压缩模型体积，主要应用于移动端部署，所以在 X86 CPU 和 Nvidia GPU 上暂不支持这类量化模型。 - -### NV GPU 上部署量化模型 - -使用 PaddleSlim 静态离线量化方法和量化训练方法产出量化模型后，可以使用 Paddle Inference 在 Nvidia GPU 上部署该量化模型。 - -Nvidia GPU 上部署常规模型的流程是：准备 TensorRT 环境、配置 Config、创建 Predictor、执行。Nvidia GPU 上部署量化模型和常规模型大体相似，需要改动的是：指定 TensorRT 配置时将 precision_mode 设置为 paddle_infer.PrecisionType.Int8，将 use_calib_mode 设为 False。 - -``` -config.enable_tensorrt_engine( - workspace_size=1<<30, - max_batch_size=1, - min_subgraph_size=5, - precision_mode=paddle_infer.PrecisionType.Int8, - use_static=False, - use_calib_mode=False) -``` - -Paddle Inference 的详细说明，请参考[文档](https://paddleinference.paddlepaddle.org.cn/product_introduction/summary.html)。 - -Nvidia GPU 上部署量化模型的详细说明，请参考[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_trt.html)。 - -### X86 CPU 上部署量化模型 - -使用 PaddleSlim 静态离线量化方法和量化训练方法产出量化模型后，可以使用 Paddle Inference 在 X86 CPU 上部署该量化模型。 - -X86 CPU 上部署量化模型，首先检查 X86 CPU 支持指令集，然后转换量化模型，最后在 X86 CPU 上执行预测。 - -Paddle Inference 的详细说明，请参考[文档](https://paddle-inference.readthedocs.io/en/latest/#)。 - -X86 CPU 上部署量化模型的详细说明，请参考[文档](https://paddle-inference.readthedocs.io/en/latest/optimize/paddle_x86_cpu_int8.html)。 - -1）检查 X86 CPU 支持指令集 - -大家可以在命令行中输入 lscpu 查看本机支持指令。 - -在支持 avx512、不支持 avx512_vnni 的 X86 CPU 上（如：SkyLake, Model name：Intel(R) Xeon(R) Gold X1XX），量化模型性能为原始模型性能的 1.5 倍左右。 - -在支持 avx512 和 avx512_vnni 的 X86 CPU 上（如：Casecade Lake, Model name: Intel(R) Xeon(R) Gold X2XX），量化模型的精度和性能最高，量化模型性能为原始模型性能的 3~3.7 倍。 - -2）转换量化模型 - -下载[转换脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py)到本地. -``` -wget https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/contrib/slim/tests/save_quant_model.py -``` - -使用脚本转换量化模型，比如： -``` -python save_quant_model.py \ - --quant_model_path=/PATH/TO/PADDLESLIM/GENERATE/MODEL \ - --int8_model_save_path=/PATH/TO/SAVE/CONVERTED/MODEL -``` - -3）执行预测 - -准备预测库，加载转换后的量化模型，创建 Predictor，进行预测。 - -注意，在 X86 CPU 预测端部署量化模型，必须开启 MKLDNN，不要开启 IrOptim（模型已经转换好）。 - -4）数据展示 - -**图像分类 INT8 模型在 Intel(R) Xeon(R) Gold 6271 上精度** - -| Model | FP32 Top1 Accuracy | INT8 Top1 Accuracy | Top1 Diff | FP32 Top5 Accuracy | INT8 Top5 Accuracy | Top5 Diff | -|:------------:|:------------------:|:------------------:|:---------:|:------------------:|:------------------:|:---------:| -| MobileNet-V1 | 70.78% | 70.74% | -0.04% | 89.69% | 89.43% | -0.26% | -| MobileNet-V2 | 71.90% | 72.21% | 0.31% | 90.56% | 90.62% | 0.06% | -| ResNet101 | 77.50% | 77.60% | 0.10% | 93.58% | 93.55% | -0.03% | -| ResNet50 | 76.63% | 76.50% | -0.13% | 93.10% | 92.98% | -0.12% | -| VGG16 | 72.08% | 71.74% | -0.34% | 90.63% | 89.71% | -0.92% | -| VGG19 | 72.57% | 72.12% | -0.45% | 90.84% | 90.15% | -0.69% | - -**图像分类 INT8 模型在 Intel(R) Xeon(R) Gold 6271 单核上性能** - -| Model | FP32 (images/s) | INT8 (images/s) | Ratio (INT8/FP32) | -|:------------:|:---------------:|:---------------:|:-----------------:| -| MobileNet-V1 | 74.05 | 216.36 | 2.92 | -| MobileNet-V2 | 88.60 | 205.84 | 2.32 | -| ResNet101 | 7.20 | 26.48 | 3.68 | -| ResNet50 | 13.23 | 50.02 | 3.78 | -| VGG16 | 3.47 | 10.67 | 3.07 | -| VGG19 | 2.83 | 9.09 | 3.21 | - -**Ernie INT8 模型在 Intel(R) Xeon(R) Gold 6271 的精度结果** - -| Model | FP32 Accuracy | INT8 Accuracy | Accuracy Diff | -| :---: | :-----------: | :-----------: | :-----------: | -| Ernie | 80.20% | 79.44% | -0.76% | - - -**Ernie INT8 模型在 Intel(R) Xeon(R) Gold 6271 上单样本耗时** - -| Threads | FP32 Latency (ms) | INT8 Latency (ms) | Ratio (FP32/INT8) | -| :--------: | :---------------: | :---------------: | :---------------: | -| 1 thread | 237.21 | 79.26 | 2.99X | -| 20 threads | 22.08 | 12.57 | 1.76X | - - -### ARM CPU 上部署量化模型 - -Paddle Lite 可以在 ARM CPU 上部署 PaddleSlim 动态离线量化方法、静态离线量化方法和量化训练方法产出的量化模型。 - -Paddle Lite 部署量化模型的方法和常规非量化模型完全相同，主要包括使用 opt 工具进行模型优化、执行预测。 - -Paddle Lite 的详细说明，请参考[文档](https://www.paddlepaddle.org.cn/lite)。 - -Paddle Lite 部署动态离线量化方法产出的量化模型，请参考[文档](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant/quant_post_dynamic.html)。 - -Paddle Lite 部署静态离线量化方法产出的量化模型，请参考[文档](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant/quant_post_static.html)。 - -Paddle Lite 部署量化训练方法产出的量化模型，请参考[文档](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant_aware.html)。 - -**模型量化前后性能对比** - -| 骁龙 855 | armv7(ms) | armv7(ms) | armv7(ms) | armv8(ms) | armv8(ms) | armv8(ms) | -|:------:|:---------:|:---------: | :-------: | :--------:| :--------:| :--------:| -| threads num| 1 | 2 | 4 | 1 | 2 | 4 | -| mobilenet_v1_fp32 | 32.19 | 18.75 | 11.02 | 29.50 | 17.50 | 9.58 | -| mobilenet_v1_int8 | 19.00 | 10.93 | 5.97 | 13.08 | 7.68 | 3.98 | -| mobilenet_v2_fp32 | 23.77 | 14.23 | 8.52 | 19.98 | 12.19 | 7.44 | -| mobilenet_v2_int8 | 17.68 | 10.49 | 5.93 | 12.76 | 7.70 | 4.36 | From 6335708a1be15907778c410df1c47a32016951c8 Mon Sep 17 00:00:00 2001 From: hu-qi Date: Thu, 16 May 2024 13:27:05 +0800 Subject: [PATCH 3/3] recove files --- docs/dev_guides/git_guides/index_cn.rst | 21 + .../git_guides/local_dev_guide_en.md | 168 +++ .../git_guides/submit_pr_guide_cn.md | 90 ++ .../git_guides/submit_pr_guide_en.md | 91 ++ docs/eval/evaluation_of_docs_system.md | 851 ++++++++++++ docs/eval/images/blanks.png | Bin 0 -> 26233 bytes docs/eval/images/code-style-01.png | Bin 0 -> 16426 bytes docs/eval/images/code-style-02.png | Bin 0 -> 22168 bytes docs/eval/images/documentation-system.png | Bin 0 -> 50544 bytes docs/eval/images/inconsistent-title.png | Bin 0 -> 27666 bytes 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docs/guides/10_contribution/index_en.rst create mode 100644 docs/guides/10_contribution/rfcs.md diff --git a/docs/dev_guides/git_guides/index_cn.rst b/docs/dev_guides/git_guides/index_cn.rst new file mode 100644 index 00000000000..da317adcdc1 --- /dev/null +++ b/docs/dev_guides/git_guides/index_cn.rst @@ -0,0 +1,21 @@ +############ +Git 操作指南 +############ + +以下将简单介绍 Git 操作指南以及 Paddle CI 手册，以帮助你快速参与到飞桨的开发中。 + +- `本地开发指南 <./local_dev_guide_cn.html>`_ : 如何在本地进行 Paddle 开发。 +- `代码风格检查指南 <./codestyle_check_guide_cn.html>`_ : 如何在本地进行代码风格检查。 +- `提交 PR 注意事项 <./submit_pr_guide_cn.html>`_ : 提交 PR 相关的注意事项。 +- `Code Review 约定 <./code_review_cn.html>`_ : 飞桨框架对于 Code Review 的一些约定介绍。 +- `Paddle CI 手册 <./paddle_ci_manual_cn.html>`_ : Paddle CI 介绍说明。 + + +.. toctree:: + :hidden: + + local_dev_guide_cn.md + codestyle_check_guide_cn.md + submit_pr_guide_cn.md + code_review_cn.md + paddle_ci_manual_cn.md diff --git a/docs/dev_guides/git_guides/local_dev_guide_en.md b/docs/dev_guides/git_guides/local_dev_guide_en.md new file mode 100644 index 00000000000..84f7e30c397 --- /dev/null +++ b/docs/dev_guides/git_guides/local_dev_guide_en.md @@ -0,0 +1,168 @@ +# Guide of local development + +You will learn how to develop programs in local environment under the guidelines of this document. + +## Requirements of coding +- Please refer to the coding comment format of [Doxygen](http://www.doxygen.nl/) +- Unit test is needed for all codes. +- Pass through all unit tests. +- Please follow [regulations of submitting codes](./code_review_cn.html). + +The following guidiance tells you how to submit code. +## [Fork](https://help.github.com/articles/fork-a-repo/) + +Transfer to the home page of GitHub [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) ,and then click button `Fork` to generate the git under your own file directory,such as 。 + +## Clone + +Clone remote git to local: + +```bash +➜ git clone https://github.com/USERNAME/Paddle +➜ cd Paddle +``` + + +## Create local branch + +At present [Git stream branch model](http://nvie.com/posts/a-successful-git-branching-model/) is applied to Paddle to undergo task of development,test,release and maintenance.Please refer to [branch regulation of Paddle](https://github.com/PaddlePaddle/docs/blob/develop/docs/design/others/releasing_process.md) about details。 + +All development tasks of feature and bug fix should be finished in a new branch which is extended from `develop` branch. + +Create and switch to a new branch with command `git checkout -b`. + + +```bash +➜ git checkout -b my-cool-stuff +``` + +It is worth noting that before the checkout, you need to keep the current branch directory clean, otherwise the untracked file will be brought to the new branch, which can be viewed by `git status` . + + +## Use `pre-commit` hook + +Paddle developers use the [pre-commit](http://pre-commit.com/) tool to manage Git pre-commit hooks. It helps us format the source code (C++, Python) and automatically check some basic things before committing (such as having only one EOL per file, not adding large files in Git, etc.). + +The `pre-commit` test is part of the unit test in CI. A PR that does not satisfy the hook cannot be submitted to Paddle. The pre-commit used by Paddle is version 1.10.4. Install `pre-commit` first and then run it in current directory： + + +```bash +➜ pip install pre-commit==1.10.4 +➜ pre-commit install +``` + +Paddle modify the format of C/C++ source code with `clang-format` .Make sure the version of `clang-format` is above 3.8. + +Note：There are differences between the installation of `yapf` with `pip install pre-commit` and that with `conda install -c conda-forge pre-commit` . Paddle developers use `pip install pre-commit` 。 + +## Start development + +I delete a line of README.md and create a new file in the case. + +View the current state via `git status` , which will prompt some changes to the current directory, and you can also view the file's specific changes via `git diff` . + + +```bash +➜ git status +On branch test +Changes not staged for commit: + (use "git add ..." to update what will be committed) + (use "git checkout -- ..." to discard changes in working directory) + modified: README.md +Untracked files: + (use "git add ..." to include in what will be committed) + test +no changes added to commit (use "git add" and/or "git commit -a") +``` + +## Build + +Please refer to [Compile From Source Code](../../../install/compile/fromsource_en.html) about more information of building PaddlePaddle source codes. + +## Test + +Any new unit testing file of the format `test_*.py` added to the directory `test/` is automatically added to the project to compile. + +Remarks: + +- **running unit tests requires compiling the entire project** and requires compiling with flag `WITH_TESTING` on i.e. `cmake paddle_dir -DWITH_TESTING=ON`. + +- **To execute a single test, you must use the ctest command**, not use `python test_*.py` + +After successfully compiling the project, run the following command to run unit tests: + +```bash +ctest -R test_mul_op -V +``` + +## Commit + +Next we cancel the modification of README.md,and submit new added test file. + +```bash +➜ git checkout -- README.md +➜ git status +On branch test +Untracked files: + (use "git add ..." to include in what will be committed) + test +nothing added to commit but untracked files present (use "git add" to track) +➜ git add test +``` + +It's required that the commit message is also given on every Git commit, through which other developers will be notified of what changes have been made. Type `git commit` to realize it. + +```bash +➜ git commit +CRLF end-lines remover...............................(no files to check)Skipped +yapf.................................................(no files to check)Skipped +Check for added large files..............................................Passed +Check for merge conflicts................................................Passed +Check for broken symlinks................................................Passed +Detect Private Key...................................(no files to check)Skipped +Fix End of Files.....................................(no files to check)Skipped +clang-formater.......................................(no files to check)Skipped +[my-cool-stuff c703c041] add test file + 1 file changed, 0 insertions(+), 0 deletions(-) + create mode 100644 233 +``` + + +## Keep the latest local repository + +It needs to keep up with the latest code of original repository(）before Pull Request. + +Check the name of current remote repository with `git remote`. + +```bash +➜ git remote +origin +➜ git remote -v +origin https://github.com/USERNAME/Paddle (fetch) +origin https://github.com/USERNAME/Paddle (push) +``` + +origin is the name of remote repository that we clone,which is also the Paddle under your own account. Next we create a remote host of an original Paddle and name it upstream. + +```bash +➜ git remote add upstream https://github.com/PaddlePaddle/Paddle +➜ git remote +origin +upstream +``` + +Get the latest code of upstream and update current branch. + +```bash +➜ git fetch upstream +➜ git pull upstream develop +``` + +## Push to remote repository + +Push local modification to GitHub(https://github.com/USERNAME/Paddle). + +```bash +# submit it to remote git the branch my-cool-stuff of origin +➜ git push origin my-cool-stuff +``` diff --git a/docs/dev_guides/git_guides/submit_pr_guide_cn.md b/docs/dev_guides/git_guides/submit_pr_guide_cn.md new file mode 100644 index 00000000000..05ae3de9c47 --- /dev/null +++ b/docs/dev_guides/git_guides/submit_pr_guide_cn.md @@ -0,0 +1,90 @@ +# 提交 PR 注意事项 + +## 提交 Pull Request + +- 请注意 commit 的数量： + +原因：如果仅仅修改一个文件但提交了十几个 commit，每个 commit 只做了少量的修改，这会给评审人带来很大困扰。评审人需要逐一查看每个 commit 才能知道做了哪些修改，且不排除 commit 之间的修改存在相互覆盖的情况。 + +建议：每次提交时，保持尽量少的 commit。可以通过`git rebase -i HEAD~3`将最新的 3 个 commit 合并成一个（你可以根据实际情况修改该数值），再 Push 到远程仓库，可以参考[rebase 用法](https://www.jianshu.com/p/4a8f4af4e803)。 + +- 请注意每个 commit 的名称：应能反映当前 commit 的内容，不能太随意。 + +- 请不要频繁 Merge develop 分支（在过 CI 时，会自动 Merge develop），这样会使 CI 重跑，更加延长 CI 通过时间。 + +- 评审人 review 过后，不允许使用 git push -f 强行提交代码，这样评审人无法看到修改前后的 diff，使评审变得困难。 + +## 完成 Pull Request PR 创建 + +切换到所建分支，然后点击 `Compare & pull request`。 + +![new_pull_request](../images/compare_pull_request.png) + +选择目标分支： + +![change_base](../images/change_base.png) + +如果解决了某个 Issue 的问题，请在该 PUll Request 的第一个评论框中加上：fix #issue_number，这样当该 PUll Request 被合并后，会自动关闭对应的 Issue。关键词包括：close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved，请选择合适的词汇。详细可参考[Closing issues via commit messages](https://help.github.com/articles/closing-issues-via-commit-messages/) + +接下来等待 review，如果有需要修改的地方，参照上述步骤更新 origin 中的对应分支即可。 + +## 签署 CLA 协议和通过单元测试 + +### 签署 CLA + +在首次向 PaddlePaddle 提交 Pull Request 时，你需要签署一次 CLA(Contributor License Agreement)协议，以保证你的代码可以被合入，具体签署方式如下： + +- 请你查看 PR 中的 Check 部分，找到 license/cla，并点击右侧 detail，进入 CLA 网站 + +
+ + + +
+ +- 请你点击 CLA 网站中的“Sign in with GitHub to agree”,点击完成后将会跳转回你的 Pull Request 页面 + +
+ + + +
+ + +### 通过单元测试 + +你在 Pull Request 中每提交一次新的 commit 后，会触发 CI 单元测试，请确认你的 commit message 中已加入必要的说明，请见[提交（commit）](./local_dev_guide_cn.html#commit) + +请你关注你 Pull Request 中的 CI 单元测试进程，它将会在几个小时内完成 + +当所需的测试后都出现了绿色的对勾，表示你本次 commit 通过了各项单元测试，你只需要关注显示 Required 任务，不显示的可能是我们正在测试的任务 + +如果所需的测试后出现了红色叉号，代表你本次的 commit 未通过某项单元测试，在这种情况下，请你点击 detail 查看报错详情，优先自行解决报错问题，无法解决的情况，以评论的方式添加到评论区中，我们的工作人员将和你一起查看 + + +## 删除远程分支 + +在 PR 被 merge 进主仓库后，我们可以在 PR 的页面删除远程仓库的分支。 + +![delete_branch](../../guides/10_contribution/img/delete_branch.png) + +也可以使用 `git push origin :分支名` 删除远程分支，如： + +```bash +➜ git push origin :my-cool-stuff +``` + + +## 删除本地分支 + +最后，删除本地分支。 + +```bash +# 切换到 develop 分支 +➜ git checkout develop + +# 删除 my-cool-stuff 分支 +➜ git branch -D my-cool-stuff +``` + +至此，我们就完成了一次代码贡献的过程。 diff --git a/docs/dev_guides/git_guides/submit_pr_guide_en.md b/docs/dev_guides/git_guides/submit_pr_guide_en.md new file mode 100644 index 00000000000..22f90a2c0d9 --- /dev/null +++ b/docs/dev_guides/git_guides/submit_pr_guide_en.md @@ -0,0 +1,91 @@ +# Guide of submitting PR to GitHub + +## Tthe submit of Pull Request + +- Please note the number of commit: + +Reason：It will bother reviewers a lot if a dozen of commits are submitted after modification of only one file and only a few modifications are updated in every commit.Reviewers have to check commit one by one to figure out the modification.And sometimes it needs to take the overlap among commits into consideration. + +Suggestion: Keep as few commits as possible for each commit. You can merge the latest 3 commits into one through `git rebase -i HEAD~3` (you can modify the value according to the actual situation).you can refer to [rebase usage](https://www.jianshu.com/p/4a8f4af4e803). + +- Pay attention to the name of every commit:It would be better to abstract the content of present commit and be not too arbitrary. + +- Please do not merge the develop branch frequently (when passing CI, it will automatically merge develop), which will make the CI run again and prolong the CI passing time. + +- After the reviewer has reviewed, it is not allowed to use git push -f to forcibly submit the code, so that the reviewer cannot see the diff before and after the modification, making the review difficult. + +## Finish Pull Request create PR + +Create an Issue to describe your problem and keep its number. + +Switch to the branch you have created and click `Compare & pull request`。 + +![new_pull_request](../images/compare_pull_request.png) + +Switch to targeted branch: + +![change_base](../images/change_base.png) + +If the issue of an Issue is resolved, please add: fix #issue_number to the first comment box of the PUll Request, so that when the PUll Request is merged, the corresponding Issue will be automatically closed. Keywords include: close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved, please choose the appropriate word. For details, please refer to + +Then please wait for review.If there is any need to make a modification,you can update corresponding branch in origin following the steps above. + + +## Sign CLA and pass unit tests + +### Sign CLA + +For the first time to submit Pull Request,you need to sign CLA(Contributor License Agreement) to ensure merge of your code.Specific steps are listed as follows: + +- Please check the Check in PR to find license/cla and click detail on the right to change into CLA website. + +
+ + + +
+ +- Please click “Sign in with GitHub to agree” in CLA website.It will change into your Pull Request page after the click. + +
+ + + +
+ + +### Pass unit tests + +Every new commit in your Pull Request will trigger CI unit tests,so please make sure that necessary comments have been included in your commit message.Please refer to [commit](./local_dev_guide_cn.html#commit) + +Please note the procedure of CI unit tests in your Pull Request which will be finished in several hours. + +Green ticks after all tests means that your commit has passed all unit tests,you only need to focus on showing the Required tasks, the ones not showing may be the tasks we are testing. + +Red cross after the tests means your commit hasn't passed certain unit test.Please click detail to view bug details and make a screenshot of bug,then add it as a comment in your Pull Request.Our stuff will help you check it. + + +## Delete remote branch + +We can delete branches of remote repository in PR page after your PR is successfully merged into master repository. + +![delete_branch](../guides/10_contribution/img/delete_branch.png) + +We can also delete the branch of remote repository with `git push origin :the_branch_name`,such as: + +```bash +➜ git push origin :my-cool-stuff +``` + +## Delete local branch + +Finally,we delete local branch + +```bash +# Switch to develop branch +➜ git checkout develop +# delete my-cool-stuff branch +➜ git branch -D my-cool-stuff +``` + +And now we finish a full process of code contribution diff --git a/docs/eval/evaluation_of_docs_system.md b/docs/eval/evaluation_of_docs_system.md new file mode 100644 index 00000000000..f446855348c --- /dev/null +++ b/docs/eval/evaluation_of_docs_system.md @@ -0,0 +1,851 @@ +# 飞桨官网文档体验报告 + +## 上下文介绍 + +### 文档体系的分类 + +按照 https://documentation.divio.com/ 中的理论，任何软件产品，都应该配套以下四类文档： + +![](./images/documentation-system.png) + +本文将基于这个文档分类系统，参考比较多个深度学习框架的文档组织方式，形成最终报告。 + +### 竞品比较 + +#### TensorFlow + + - Tutorial：[Tutorial](https://www.tensorflow.org/tutorials) + - GUIDE：[Guide](https://www.tensorflow.org/guide) + - EXPLANATION：除 [ML Glossary](https://developers.google.com/machine-learning/glossary/tensorflow) 外，没有显式存在。 + - REFERENCE： [API 文档](https://www.tensorflow.org/versions) + +TensorFlow 的文档规划，比较直接地匹配了本文所介绍的分类标准。 + +具体的文档结构如下： + +**Tutorials** + +- Quickstart for beginners +- Quickstart for experts +- ML basics with Keras + - Basic image classfication + - Basic text classification + - Text classification with TF Hub + - Regression + - Overfit and underfit + - Save and load + - Tune hyperprameters with Keras Tuner + - More examples on kera.io +- Load and preprocess data + - Images + - CSV + - Numpy + - pandas.DataFrame + - TFRecord and tf.Example + - Additional formats with tf.io + - Text + - More text loading +- Customization + - Tensors and operations + - Custom layers + - Custom traning: walkthrough +- Distributed training + - Distributed training with Kereas + - Distributed training with DTensors + - Using DTensors with Keras + - Custom training loops + - Multi-worker training with Keras + - Multi-worker training with CTL + - Parameter Server Training + - Save and load + - Distributed input +- Images + - Convolutional Neural Network + - Image classification + - Transfer learning and fine-tuning + - Transfer learning with TF Hub + - Data Augmentaion + - Image segmentation + - Object detection with TF Hub +- Text + - Word embeddings + - Word2Vec + - Text classification with an RNN + - classify Text with BERT + - Solve GLUE tasks using BERT on TPU + - Neural machine translation with attention + - Image captioning +- Audio + - Simple audio recognition + - Transfer learning for audio recognition + - Generate music with an RNN +- Structured data + - Classifying structured data with preprocessing layers + - Classfication on imbalanced data + - Time series forecasting + - Decision forest models + - Recommenders +- Generative + - Neural style transfer + - DeepDream + - DCGAN + - Pix2Pix + - CycleGAN + - Adversarial FGSM + - Intro to Autoencoders + - Variational Autoencoder +- Model Understanding + - Integrated gradients + - Uncertainty quantification with SNGP + - Probabilistic regression +- Reinforcement learning + - Actor-Critic method + - TensorFlow agents +- tf.Estimator + - Premade estimator + - Linear model + - Keras model to Estimator + - Multi-worker training with Estimator + - Feature columns + +**Guide** + +- TensorFlow basics + - Overview + - Tensors + - Variables + - Automatic differentiation + - Graphs and functions + - Modules, layers, and models + - Training loops +- Keras + - The Sequnetial model + - The Functional API + - Training and evaluation with the built-in methods + - Making new Laayers and Models via subclassing + - Save and load Kears models + - Working with preprocessing layers + - Customize what happens in Model.fit + - Writing a training loop from scratch + - Recurrent Neural Networks(RNN) with Keras + - Masking and padding wiht Keras + - Writing your own callbacks + - Transfer learning and fine-tuning + - Training Keras models with TensorFlow Cloud +- TensorFlow in depth + - Tensor slicing + - Advanced autodiff + - Ragged tensor + - Sparse tensor + - Random number generation + - NumPy API + - DTensor concepts + - Thinking in TensorFlow 2 +- Customization + - Create an op + - Extension types +- Data input pipelines + - tf.data + - Optimize pipeline performance + - Analyze pipeline performance +- Save a model + - Checkpoint + - SavedModel +- Accelerators + - Distributed training + - GPU + - TPU +- Performance + - Better performance with tf.function + - Profile TensorFlow performance + - Optimize GPU Performance + - Graph optimizaition + - Mixed precision + + +#### PyTorch + + - Tutorial：https://pytorch.org/tutorials/beginner/basics/intro.html + - GUIDE：https://pytorch.org/tutorials/recipes/recipes_index.html + - EXPLANATION：没有显式存在，分散在 API 文档中。 + - REFERENCE：[API 文档](https://pytorch.org/docs/stable/index.html) + +[PyTorch 的文档](https://docs.pytorch.org) 分为 API 文档和 Tutorials 两大类。但实际 Tutorials 中可以继续分为 Learn the Basics 和 PyTorch Recipes 两大类。 + +并且，PyTorch 在自己的 API 文档首页中有 Notes。在必要时，也会在模块 API 开始做背景介绍，这些内容可以归为 “Explanation” 象限。 + +- Introduction to PyTorch + - Learn the Basics + - Quickstart + - Tensors + - Datasets & Dataloaders + - Transforms + - Build the Neural Network + - Automatic Differentiation with torch.autograd + - Optimization Model Parameters + - Save and Load the Model +- Introduction to PyTorch on YouTube + - Introduction to PyTorch - YouTube Series + - Introduction to PyTorch + - Introduction to PyTorch Tensors + - The Fundamentals of Autograd + - Building Models with PyTorch + - PyTorch TensorBoard Support + - Traning wiht PyTorch + - Model Understanding with Captum +- Learning PyTorch + - Deep Learning with PyTorch: A 60 Minute Blitz + - Learing PyTorch with Examples + - What is torch.nn really? + - Visualizing Models, Data, and Training with TensorBoard +- Image and Video + - TorchVision Object Detection Finetuning Tutorial + - Transfer Learning for Computer Vision Tutorial + - Adversarial Example Generation + - DCGAN Tutorial + - Spatial Tansformer Networks Tutorial + - Optimizaing Vision Transformer Model for Deployment +- Audio + - Audio I/O + - Audio Resampling + - Audio Data Augmentation + - Audio Feature Extractions + - Audio Feature Augmentation + - Audio Datasets + - Speech Recognition with Wav2Vec2 + - Speech Command Classification with torchaudio + - Text-to-speech with torchaudio + - Forced Alignment with Wav2Vec2 +- Text + - Language Modeling with nn.Transformer and TorchText + - NLP From Scratch: Classifying Names with a Character-Level RNN + - NLP From Scratch: Generating Names with a Character-Level RNN + - NLP From Scratch: Translation with a Sequence to Sequence Network and Attention + - Text classification with the torchtext library Language Translation with nn.Transformer and torchtext +- Deploying PyTorch Models in Production + - Deploying PyTorch in Python via a REST API with Flask + - Introduction to TorchScript + - Loading a TorchScript Model in C++ + - (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime + - Real Time Inference on Raspberry Pi 4 +- Code Transforms with FX + - Building a Convolution/Batch Norm fuser in FX + - Building a Simple CPU Performance Profiler with FX +- Frontend APIs + - Channels Last Memory Format in PyTorch + - Forward-mode Automatic Differentiation + - Using the PyTorch C++ Frontend + - Dynamic Parallelism in TorchScript + - Autograd in C++ Frontend +- Extending PyTorch + - Double Backward with Custom Functions + - Fusing Convolution and Batch Norm using Custom Function + - Custom C++ and CUDA Extensions + - Extending TorchScript with Custom C++ Operators + - Extending TorchScript with Custom C++ Classes + - Registering a Dispatched Operator in C++ + - Extending dispatcher for a new backend in C++ +- Model Optimization + - Profiling your PyTorch Module + - PyTorch Profiler with TensorBoard + - Hyperparameter tuning with Ray Tune + - Optimizing Vision Transformer Model for Deployment Parametrization Tutorial + - Pruning Tutorial + - Dynamic Quantization on an LSTM Word Language Model + - Dynamic Quantization on BERT + - Quantized Transfer Learning for Computer Vision Tutorial + - Static Quantization with Eager Model in PyTorch + - Grokking PyTorch Intel CPU performance from first principles +- Parallel and Distributed Tranining + - PyTorch Distributed Overview + - Single-Machine Model Parallel Best Practices + - Getting Started with Distributed Data Parallel + - Writing Distributed Applications with PyTorch + - Getting Started with Fully Sharded Data Prallel + - Customize Process Group Backends Using Cpp Extension + - Getting Started with Distributed RPC Framework + - Implementing a Parameter Server Using Distributed RPC Framework + - Distributed Pipeline Parallelsim using RPC + - Implementing Batch RPC Processing Using Asynchronous Executions + - Combining Distributed DataPrallel with Distributed RPC Framework + - Training Transformer models using Pipeline Parallelism + - Training Transformer models using Distributed Data Parallel and Pipeline Parallelism + - Distributed Training with Uneven Inputs Using the Join Context Manager +- Moible + - Image Segmentation DeepLabV3 on iOS + - Image Segmentation DeepLabV3 on Android +- Recommendation Systems + - Introduction to TorchRec + + + +#### MindSpore + + - Tutorial：在 [教程](https://www.mindspore.cn/tutorials/zh-CN/r1.6/index.html) 前部分和 [编程指南](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/index.html) 的前半部分话题中 + - GUIDE：在 [教程](https://www.mindspore.cn/tutorials/zh-CN/r1.6/index.html) 后部分和 [编程指南](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/index.html) 的后半段话题以及 [开发者精华分享](https://bbs.huaweicloud.com/forum/forumdisplay-fid-1076-orderby-lastpost-filter-typeid-typeid-1255sub.html) 中 + - EXPLANATION：[规格和说明](https://www.mindspore.cn/docs/note/zh-CN/r1.6/index.html) 及 [FAQ](https://www.mindspore.cn/docs/faq/zh-CN/r1.6/index.html) + - REFERENCE：[API 文档](https://www.mindspore.cn/docs/api/zh-CN/r1.6/index.htmls) + +MindSpore 的有自己独立的文档分类标准和风格，所以硬套本文提及的文档分类标准，结果会显得有些复杂。以上所列的各类文档中，《开发者精华分享》是比较独特的一个栏目，他更像是 MindSpore 搭建的开源社区平台，吸收了用户贡献的各种经验，包括 Numpy 的使用，MindSpore 的安装问题如何解决等社区贡献的知识。 + +**初学入门** + +- 初学教程 + - 基本介绍 + - 快速入门 + - 张量 Tensor + - 数据处理 + - 创建网络 + - 自动微分 + - 模型训练 + - 保存与加载 + - 推理与部署 +- 进阶 + - 进阶案例：线性拟合 + - 数据处理 + - 网络构建 + - 训练与评估 + - 动态图与静态图 + +**应用实践** + +- 计算机视觉 + - ResNet50 网络进行图像分类 + - 图像分类迁移学习 + - 模型对抗攻击 + - 生成式对抗网络 +- 自然语言处理 + - 使用 RNN 实现情感分类 + - LSTM+CRF 实现序列标注 + +**深度开发** + +- 数据处理 + - 自动数据增强 + - 轻量化数据处理 + - 单节点数据缓存 + - 优化数据处理 + +- 算子执行 + - 算子分类 + - 运算重载 + - 自定义算子（CPU） + - 自定义算子（GPU） + - 自定义算子（Ascend） + - 自定义算子（基于 Custom 表达） + +- 模型推理 + - 模型推理总览 + - GPU/CPU 推理 + - Ascend 910 AI 处理器上推理 + - Ascend 310 AI 处理器上使用 MindIR 模型进行推理 + - Ascend 310 AI 处理器上使用 AIR 模型进行推理 + +- 调试调优 + - 查看中间文件 + - Dump 功能调试 + - 自定义调试信息 + - 调用自定义类 + - 算子增量编译 + - 算子调优工具 + - 自动数据加速 + - 固定随机性以复现脚本运行结果 + +- 分布式并行 + 分布式并行总览 + 分布式集合通信原语 + 分布式并行训练基础样例（Ascend） + 分布式并行训练基础样例（GPU） + 分布式推理 + 保存和加载模型（HyBrid Parallel 模式） + 分布式并行训练 Transformer 模型 + 鹏程·盘古模型网络多维度混合并行解析 + 分布式故障恢复 + +- 其他特性 + - 混合精度 + - 梯度累积算法 + - 自适应梯度求和算法 + - 降维训练算法 + +- 设计 + - 设计白皮书 + - 全场景统一 + - 函数式微分编程 + - 动静态图结合 + - 异构并行训练 + - 分布式并行 + - 中间表达 MindIR + - 高性能数据处理引擎 + - 图算融合加速引擎 + - 二阶优化 + - 可视化调试调优 + - 安全可信 + - 术语 + +- 迁移指南 + - 概述 + - 准备工作 + - 网络脚本分析 + - 网络脚本开发 + - 网络调试 + - 精度调试 + - 性能调试 + - 推理执行 + - 网络迁移调试实例 + - 常见问题 + +### 完备性定义 + +综合 Paddle 及其它竞品，先罗列一个较为完备的“知识点体系”。即如果是各个级别的用户，面向他们应该提供哪类信息，做一个全集式的罗列。 + +- 初级用户：初级用户定位是未有使用深度学习框架完成项目的经验，如高校低年级学生 +- 中级用户：中级用户定位是通常意义上的算法工程师，在日常的项目中需要使用深度学习框架，但还不涉及较为“高级”的特性。如独立参与比赛的高校学生，工业界的初级、中级算法工程师。 +- 高级用户：需要紧随前沿使用框架高级特性的工业届、学术界用户。如复杂的并行技术、算子融合、AMP 等高级优化技术。 + +对应的信息分类和罗列： + +- 初级： + - 基本数据（Tensor）和基本算子（Op） + - 数据加载 + - 如何组网 + - 如何训练 + - 保存与加载模型 + - 可视化、调优技巧 + - 自动微分 + +- 中级： + - 动态图与静态图的转换 + - 部署相关 + - CV 领域的实践指南 + - NLP 领域的实践指南 + - 推荐系统领域的实践指南 + - 强化学习领域的实践指南 + - 移动端相关 + - 从其它框架至本框架的迁移 + +- 高级： + - 如何自定义算子 + - 分布式训练 + - 框架设计文档 + +## Paddle 文档体系分析 + +现有 Paddle 文档，结构如下： + +**文档** + +- 整体介绍 + - 基本概念 + - 升级指南 + - 版本迁移工具 +- 模型开发 + - 10 分钟快速上手飞桨 + - 数据集的定义和加载 + - 数据预处理 + - 模型组网 + - 训练与预测验证 + - 单机多卡训练 + - 自定义指标 + - 模型保存与载入 + - 模型导出 ONNX 协议 +- VisualDL 工具 + - VisualDL 工具简介 + - VisualDL 使用指南 +- 动态图转静态图 + - 使用样例 + - 转换原理 + - 支持语法 + - 案例解析 + - 报错调试 +- 推理部署 + - 服务器部署 + - 移动端/嵌入式部署 + - 模型压缩 +- 分布式训练 + - 分布式训练开始 + - 使用 FleetAPI 进行分布式训练 +- 自定义算子 + - 自定义原生算子 + - 原生算子开发注意事项 + - 自定义外部算子 + - 自定义 Python 算子 + - Kernel Primitive API + - API 介绍 + - API 示例 +- 性能调优 + - 飞桨模型量化 +- 算子映射 + - Paddle 1.8 与 Paddle 2.0 API 映射表 + - PyTorch-PaddlePaddle API 映射表 +- 硬件支持 + - 飞桨产品硬件支持表 + - 昆仑 XPU 芯片运行飞桨 + - 海光 DCU 芯片运行飞桨 + - 昇腾 NPU 芯片运行飞桨 +- 参与开发 + - 本地开发指南 + - 提交 PR 注意事项 + - FAQ +- 环境变量 FLAGS + - cudnn + - 数值计算 + - 调试 + - check nan inf 工具 + - 设备管理 + - 分布式 + - 执行器 + - 存储管理 + - 曻腾 NPU + - 其它 + +**应用实践** + +- 快速上手 + - hello paddle：从普通程序走向机器学习程序 + - 动态图 + - 飞桨高层 API 使用指南 + - 模型保存及加载 + - 使用线性回归预测波士顿房价 +- 计算机视觉 + - 使用 LeNet 在 MNIST 数据集实现图像分类 + - 使用卷积神经网络进行图像分类 + - 基于图片相似度的图片搜索 + - 基于 U-Net 卷积神经网络实现宠物图像分割 + - 通过 OCR 实现验证码识别 + - 通过 Sub-Pixel 实现图像超分辨率 + - 人脸关键点检测 + - 点云处理：实现 PointNet 点云分类 +- 自然语言处理 + - 用 N-Gram 模型在莎士比亚文集中训练 word embedding + - IMDB 数据集使用 BOW 网络的文本分类 + - 使用预训练的词向量完成文本分类任务 + - 使用注意力机制的 LSTM 的机器翻译 + - 使用序列到序列模型完成数字加法 +- 推荐 + - 使用协同过滤实现电影推荐 +- 强化学习 + - 强化学习——Actor Critic Method + - 强化学习——Advantage Actor-Critic(A2C) + - 强化学习——Deep Deterministic Policy Gradient (DDPG) +- 时序数据 + - 通过 AutoEncoder 实现时序数据异常检测 +- 动转静 + - 使用动转静完成以图搜图 + + +### 完备性分析 + +根据以上对同类软件的文档做了调研罗列的基础上，对它们的知识点，以及知识点对应的文章，做了初步的统计。 + +有几点说明： + +- TensorFlow、PaddlePaddle 因为已经将部署、移动端推理独立成了产品，所以以上表格中统计数据可能不准确，或未做统计 +- PaddlePaddle 的 [教程](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3465990) 一栏中也包含了大量的文章，但是未做统计（因为不在 `docs` 仓库中），不过会在下文中的“组织结构分析中”纳入考量 + +| | TensorFlow | 数量 | PyTorch | 数量 | MindSpore | 数量 | PaddlePaddle | 数量 | +| ---------------------------- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ---- | ------------------------------------------------------------ | ------ | +| 基本数据（Tensor）和基本算子 | Tensors Variables Tensor slicing Ragged tensor Sparse tensor DTensor concepts | 6 | Tensors Transforms Introduction to PyTorch Tensors | 3 | 张量 Tensor | 1 | Tensor 概念介绍 | 1 | +| 数据加载与预处理 | Images CSV Numpy pandas.DataFrame TFRecord and tf.Example Additional formats with tf.io Text More text loading Classifying structured data with preprocessing layers Classfication on imbalanced data Time series forecasting Decision forest models | 13 | Datasets & Dataloaders | 1 | 数据处理 数据处理（进阶） 自动数据增强 轻量化数据处理 单节点数据缓存 优化数据处理 | 6 | 数据集的定义和加载 数据预处理 | 2 | +| 如何组网 | Modules, layers, and models | 1 | Build the Neural Network Building Models with PyTorch What is torch.nn really? Learing PyTorch with Examples | 4 | 创建网络 网络构建 | 2 | 模型组网 飞桨高层 API 使用指南 层与模型 | 3 | +| 如何训练 | Training loops NumPy API Checkpoint SavedModel | 4 | Optimization Model Parameters Traning wiht PyTorch | 2 | 模型训练 训练与评估 | 2 | 训练与预测验证 自定义指标 | 2 | +| 保存与加载模型 | Save and load Save and load(Distrbuted Training) | 2 | Save and Load the Model | 1 | 保存与加载 | 1 | 模型保存与载入 模型保存及加载（应用实践） | 2 | +| 可视化、调优技巧 | Overfit and underfit Tune hyperprameters with Keras Tuner Better performance with tf.function Profile TensorFlow performance Graph optimizaition Optimize GPU Performance Mixed precision | 7 | PyTorch TensorBoard Support Model Understanding with Captum Visualizing Models, Data, and Training with TensorBoard Profiling your PyTorch Module PyTorch Profiler with TensorBoard Hyperparameter tuning with Ray Tune Optimizing Vision Transformer Model for Deployment Parametrization Tutorial Pruning Tutorial Grokking PyTorch Intel CPU performance from first principles | 11 | 查看中间文件 Dump 功能调试 自定义调试信息 调用自定义类 算子增量编译 算子调优工具 自动数据加速 固定随机性以复现脚本运行结果 | 8 | VisualDL 工具简介 VisualDL 使用指南 飞桨模型量化 | 3 | +| 自动微分 | Automatic differentiation Advanced autodiff | 2 | Automatic Differentiation with torch.autograd The Fundamentals of Autograd | 2 | 自动微分 | 1 | 自动微分 | 1 | +| 动态图与静态图 | Graphs and functions | 1 | （torchscript 其实是静态图，不过归类到部署中了） | 0 | 动态图与静态图 | 1 | 使用样例 转换原理 支持语法 案例解析 报错调试 动态图 使用动转静完成以图搜图 | 7 | +| 部署相关 | https://www.tensorflow.org/tfx/tutorials 下的 21 篇文章 https://www.tensorflow.org/tfx/guide 下的 30+文章 | 50+ | Deploying PyTorch in Python via a REST API with Flask Introduction to TorchScript Loading a TorchScript Model in C++ (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime Real Time Inference on Raspberry Pi 4 | 6 | 推理与部署 模型推理总览 GPU/CPU 推理 Ascend 910 AI 处理器上推理 Ascend 310 AI 处理器上使用 MindIR 模型进行推理 Ascend 310 AI 处理器上使用 AIR 模型进行推理 | 7 | 服务器部署 移动端/嵌入式部署 模型压缩 https://www.paddlepaddle.org.cn/lite/v2.10/guide/introduction.html 下 50+ 篇文章 | 50+ | +| CV 领域相关 | Basic image classfication Convolutional Neural Network Image classification Transfer learning and fine-tuning Transfer learning with TF Hub Data Augmentaion Image segmentation Object detection with TF Hub Neural style transfer DeepDream DCGAN Pix2Pix CycleGAN Adversarial FGSM Intro to Autoencoders Variational Autoencoder | 16 | TorchVision Object Detection Finetuning Tutorial Transfer Learning for Computer Vision Tutorial Adversarial Example Generation DCGAN Tutorial Spatial Tansformer Networks Tutorial Optimizaing Vision Transformer Model for Deployment Quantized Transfer Learning for Computer Vision Tutorial | 7 | ResNet50 网络进行图像分类 图像分类迁移学习 模型对抗攻击 生成式对抗网络 | 4 | 使用 LeNet 在 MNIST 数据集实现图像分类 使用卷积神经网络进行图像分类 基于图片相似度的图片搜索 基于 U-Net 卷积神经网络实现宠物图像分割 通过 OCR 实现验证码识别 通过 Sub-Pixel 实现图像超分辨率 人脸关键点检测 点云处理：实现 PointNet 点云分类 | 7 | +| NLP 领域相关 | Basic text classification Text classification with TF Hub Word embeddings Word2Vec Text classification with an RNN classify Text with BERT Solve GLUE tasks using BERT on TPU Neural machine translation with attention Image captioning | 9 | Language Modeling with nn.Transformer and TorchText NLP From Scratch: Classifying Names with a Character-Level RNN NLP From Scratch: Generating Names with a Character-Level RNN NLP From Scratch: Translation with a Sequence to Sequence Network and Attention Text classification with the torchtext library Language Translation with nn.Transformer and torchtext Dynamic Quantization on an LSTM Word Language Model Dynamic Quantization on BERT | 8 | 使用 RNN 实现情感分类 LSTM+CRF 实现序列标注 | 2 | 用 N-Gram 模型在莎士比亚文集中训练 word embedding IMDB 数据集使用 BOW 网络的文本分类 使用预训练的词向量完成文本分类任务 使用注意力机制的 LSTM 的机器翻译 使用序列到序列模型完成数字加法 | 5 | +| 语音领域相关 | | | Audio I/O Audio Resampling Audio Data Augmentation Audio Feature Extractions Audio Feature Augmentation Audio Datasets Speech Recognition with Wav2Vec2 Speech Command Classification with torchaudio Text-to-speech with torchaudio Forced Alignment with Wav2Vec2 | 10 | | 0 | | 0 | +| 推荐领域相关 | Recommenders | 1 | Introduction to TorchRec | 1 | | 0 | 使用协同过滤实现电影推荐 | 1 | +| 强化学习领域相关 | Actor-Critic method TensorFlow agents | 2 | 0 | 0 | | 0 | 强化学习——Actor Critic Method 强化学习——Advantage Actor-Critic(A2C) 强化学习——Deep Deterministic Policy Gradient (DDPG) | 3 | +| 移动端相关 | 独立的栏目 https://www.tensorflow.org/lite | 10+ | Image Segmentation DeepLabV3 on iOS Image Segmentation DeepLabV3 on Android | 2 | | 0 | Paddle Lite 中独立存在 | 未统计 | +| 框架之间的迁移相关 | | | | 0 | 概述 准备工作 网络脚本分析 网络脚本开发 网络调试 精度调试 性能调试 推理执行 网络迁移调试实例 常见问题 | 10 | Paddle 1.8 与 Paddle 2.0 API 映射表 PyTorch-PaddlePaddle API 映射表 版本迁移工具 | 3 | +| 自定义算子 | Tensors and operations Custom layers Custom traning: walkthrough Create an op Extension types | 5 | Double Backward with Custom Functions Fusing Convolution and Batch Norm using Custom Function Custom C++ and CUDA Extensions Extending TorchScript with Custom C++ Operators Extending TorchScript with Custom C++ Classes Registering a Dispatched Operator in C++ Extending dispatcher for a new backend in C++ | 7 | 算子分类 运算重载 自定义算子（CPU） 自定义算子（GPU） 自定义算子（Ascend） 自定义算子（基于 Custom 表达） | 6 | 自定义原生算子 原生算子开发注意事项 自定义外部算子 自定义 Python 算子 API 介绍 API 示例 本地开发指南 提交 PR 注意事项 FAQ | 9 | +| 分布式训练 | Distributed training with Kereas Distributed training with DTensors Using DTensors with Keras Custom training loops Multi-worker training with Keras Multi-worker training with CTL Parameter Server Training Distributed input Distributed training | 9 | PyTorch Distributed Overview Single-Machine Model Parallel Best PracticesGetting Started with Distributed Data Parallel Writing Distributed Applications with PyTorch Getting Started with Fully Sharded Data Prallel Customize Process Group Backends Using Cpp Extension Getting Started with Distributed RPC Framework Implementing a Parameter Server Using Distributed RPC Framework Distributed Pipeline Parallelsim using RPC Implementing Batch RPC Processing Using Asynchronous Executions Combining Distributed DataPrallel with Distributed RPC Framework Training Transformer models using Pipeline Parallelism Training Transformer models using Distributed Data Parallel and Pipeline Parallelism Distributed Training with Uneven Inputs Using the Join Context Manager | 16 | 分布式并行总览 分布式集合通信原语 分布式并行训练基础样例（Ascend） 分布式并行训练基础样例（GPU） 分布式推理 保存和加载模型（HyBrid Parallel 模式） 分布式并行训练 Transformer 模型 鹏程·盘古模型网络多维度混合并行解析 分布式故障恢复 | 9 | 单机多卡训练 分布式训练开始 使用 FleetAPI 进行分布式训练 | 3 | +| 框架设计文档 | Random number generation | 1 | 分散在 API 文档、源码中，其实比较丰富。30+ | 30+ | 设计白皮书 全场景统一 函数式微分编程 动静态图结合 异构并行训练 分布式并行 中间表达 MindIR 高性能数据处理引擎 图算融合加速引擎 二阶优化 可视化调试调优 安全可信 术语 | 13 | | 0 | +| 其它 | Integrated gradients Uncertainty quantification with SNGP Probabilistic regression Keras 一级标题下的 13 篇文章 Thinking in TensorFlow 2 Data input pipelines 一级标题下的 3 篇 GPU TPU | 20 | Learn the Basics Quickstart Deep Learning with PyTorch: A 60 Minute Blitz Building a Convolution/Batch Norm fuser in FX Building a Simple CPU Performance Profiler with FX Channels Last Memory Format in PyTorch Forward-mode Automatic Differentiation Using the PyTorch C++ Frontend Dynamic Parallelism in TorchScript Autograd in C++ Frontend Static Quantization with Eager Model in PyTorch | 11 | 基本介绍 快速入门 进阶案例：线性拟合 混合精度 梯度累积算法 自适应梯度求和算法 降维训练算法 | 7 | 10 分钟快速上手飞桨 使用线性回归预测波士顿房价 模型导出 ONNX 协议 飞桨产品硬件支持表 昆仑 XPU 芯片运行飞桨 海光 DCU 芯片运行飞桨 昇腾 NPU 芯片运行飞桨 环境变量 FLAGS 下 9 篇 hello paddle：从普通程序走向机器学习程序 通过 AutoEncoder 实现时序数据异常检测 广播介绍 自动混合精度训练 梯度裁剪 升级指南 | 20+ | + +可以看除，PaddlePaddle 在文档上是比较完备的，在本文划分的 19 个具体领域中的 17 个领域中都已有文档，包括： + +- 基本数据（Tensor）和基本算子 +- 数据加载与预处理 +- 如何组网 +- 如何训练 +- 保存与加载模型 +- 可视化、调优技巧 +- 自动微分 +- 动态图与静态图 +- 部署相关 +- CV 领域相关 +- NLP 领域相关 +- 推荐领域相关 +- 强化学习领域相关 +- 移动端相关 +- 框架之间的迁移相关 +- 自定义算子 +- 分布式训练 + +并且，与其它框架相比，有一些独特的文章，它们归类到了“其它”中，比如与国产芯片的适配，Paddle 的环境变量的设置及意义等。 + +但是在个别领域，还比较薄弱甚至空缺，主要是： + +- 语音领域是空缺的 +- 与 PyTorch 相比，分布式领域的文档还比较薄弱。PyTorch 有 13 篇分布式有关的文档，涵盖了分布式训练的基本概念、设备之间的通信、不同模块的使用、不同分布式策略的选择和使用。而 Paddle 只有三篇，并且主要集中在 API 接口的介绍，没有结合分布式策略详细介绍，对于没有太多分布式训练经验的人而言，容易困惑。 +- 框架设计文档在官方网站上是空缺的，像 PyTorch、TensorFlow 都有深度介绍框架设计的文档，这对像参与开源建设或者自定义框架的高阶用户，还是很必要的。 + +### 行文风格分析 + +整体而言，PaddlePaddle 的文档保持了较高的水准。不过因为精力有限，笔者虽然有较长的 Paddle 使用经验，但是也没有对所有文档做“深入分析”，在此仅针对一些有代表的案例，发表自己的看法，讨论下是否有改进的必要。 + +**代码风格的统一** + +首先，我认为应该在形式上，尽量追求统一，形式上的“统一美感”，可以给读者留下好的第一印象。 + +文章中有些地方出现了代码高亮格式的不一致，甚至是同一篇文章中就有高亮风格不一致的地方，如 [自定义指标](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/07_customize_cn.html#zidingyizhibiao) 一文中，一些代码中 docstring 有高亮，一些没有： + +![](./images/code-style-01.png) +![](./images/code-style-02.png) + +**标题的统一** + +其次，有些二级标题，在导航栏的显示名称，与导航页面的显示名称，是不一致的： + +![](./images/inconsistent-title.png) + +**文章的新旧更迭** + +还有一些文章的链接，已经失效了，比如 [本地开发指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/10_contribution/local_dev_guide_cn.html) 中： + +![](./images/invalid-url.png) + +有些文章中引用的链接，虽然没有失效，但是其实不应该引用，或者需要做更新。比如 [使用动转静完成以图搜图](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/jit/image_search_with_jit.html) 一文中引用了 [基于图片相似度的图片搜索](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/cv_case/image_search/image_search.html)，但是后者其实已经是一篇“孤岛文章”，并没有被纳入到 Paddle 的文档体系中： + +![](./images/outdated-article.png) + +很可能应该被 [基于图片相似度的图片搜索](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/image_search.html) 替换。 + +这类问题，仔细找的话还可以找到不少，不过定位所有这类问题，超出了本文的范围，建议可以组织人手做一次系统的摸排。 + +**空格的细节** + +中文和英文之间，是否有空格，应该没有严格的规定，但是在官方文档中，应该统一，要么都有空格，要么都没有。如果混用的话，也会有不协调的感觉。 + +![](./images/blanks.png) + + +### 内容分析 + +#### 概述 + +按照 [The documentation system](https://documentation.divio.com/) 中对文档的划分（见本文开头图片），每类文档都有自己的特点和写作标准。而相邻区间的文档，又因为有相似的性质，所以容易“混乱”。 + +比如 Tutorial 和 Guide 是两类相似，但是又不同的文档。一般而言，Tutorial 的风格更像一堂课，需要动手操作 steps，也需要介绍“知识概念”。而 Guide 是解决问题为导向的，需要一系列的 steps，以及较为直接的解决问题。 + +如果在 Guide 中引入了太多的“知识概念”，会让急于解决具体问题的读者，感到重点不明显。 + +而如果在 Tutorial 中，不顾及读者的已有知识体系，单纯地“灌输”操作步骤，就容易让读者失去阅读和学习的乐趣，过早地产生放弃继续阅读的想法。 + +再比如，Reference 和 Guide 也是相邻的象限，不过 Guide 更侧重实际操作，而 Reference 注重理论知识。如果不注意它们的区分，比如在 Guide 中只介绍接口参数，却不举具体的接口使用的例子，容易让读者“心中没底”；在 Reference （如 API 文档）中如果不追求 “dry & clean” 的风格，以及完备的信息，就容易让读者怀疑资料的权威性。 + + +因为 Paddle 中的一些文章，并没有显式地说明到底是哪种类型，除了 API 文档明显应该属于 Reference 外，其它的笔者只能根据自己的感受主观上判断，并且笔者认为几乎均为 Tutorial 或者 Guide 类别，而没有显式的 Explanation 类别的文档。以下内容依据 Tutorial 或 Guide 的要求，来做具体案例分析，不一定准确，仅供参考。 + +#### 具体案例 + +Paddle 当前文档中的 Tutorial，特别是初级内容部分，都很好的遵循了 Tutorial 的写作要求，如 [基本概念](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/index_cn.html) 中的所有文章和 [模型开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/index_cn.html) 中的大部分文章。 + +但是，对于一些类别为 Guide 的文章，往往还有提升空间。以一级标题 [动态图转静态图](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/index_cn.html) 中的文章为例，我们继续讨论： + +**不必要的拆黑箱** + +在 [使用样例](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/basic_usage_cn.html) 一文中，除了提供直接可运行的 steps 之外，还提供了对于用户而言，比较复杂的 “@to_static 的基本执行流程如下图”： + +![](https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/guides/04_dygraph_to_static/images/to_static_train.png) + +但是这个流程图个人认为可以在此省去，或者直接移动到 [转换原理](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/principle_cn.html) 一文中。 + +**缺乏 steps** + +在 [支持语法](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/principle_cn.html) 一文中，几乎都是大量罗列，而比较缺少代码的实例。 + +当然，静态图所支持的语法限制，本身就难“正向”做展示。在这里，我建议可以考虑，干脆“反其道行之”，展示“反面操作”，即如果使用的语法超出了静态图的范围，会报怎么样的错误，如何纠正。这样的“做中学”，可能可以给读者更深的印象。而将现有的这种大而全的正想罗列，放入到 API 文档中。 + +**缺简单易复现的环境** + +在 [报错调试](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/04_dygraph_to_static/debugging_cn.html) 一文中，细致地教用户如何看错误信息，以及介绍相关的多种工具，这都是非常友好的。但是美中不足，并没有配套的仓库。如果能为用户专门准备一个配套的“试错仓库”，用户克隆后，可以直接复现这类错误，并且根据文档定位、纠正，这样可能会更友好。 + + + + +### 组织结构分析 + +从组织结构角度分析，以我个人的感受，飞桨还有一些值得讨论，和可能改进的地方。 + +#### 从 Paddle 文档一级标题看完备性分析 + +在上文 [完备性分析](#完备性分析) 部分，已经 Paddle 文档体系覆盖内容的分析。在本节，笔者尝试讨论，如何对文档的标题做重构，得到更好的文档结构，个人看法，仅供参考。之所以认为这样更合理，是可以为读者提供“一站式”服务，而现有的 Paddle 文档体系，同类别的文档，有时会分散在多个栏目中；或者某个重要的内容，藏的目录级别较深（如 “整体介绍/升级指南/使用高层 API”），不容易被读者发现。 + +建议，可以分为以下几个 “栏目”，每个栏目中有若干篇文章（一级标题）。 + +- 使用 Paddle +- Paddle 与 CV +- Paddle 与 NLP +- Paddle 与语音 +- Paddle 与推荐系统 +- Paddle 与强化学习 +- 自定义扩展 Paddle +- 框架设计文档 +- Cookbook + +它们与现有文档体系的对应关系，以下分开阐述。 + +**使用 Paddle** + +在这个栏目下，针对上手 Paddle 的用户，分模块介绍 Paddle 的各个功能。可以包括： + +- 快速上手（对应现有的《10 分钟快速上手飞桨》，同时应注意链在文章内连接重构后的其它更细节的文章） +- Tensor（对应现有的《Tensor 概念介绍》） +- 数据加载与预处理（对应现有的《数据集定义和加载》、《数据预处理》） +- 搭建模型（对应现有的《Paddle 中的模型与层》、《模型组网》） +- 模型训练（对应现有的《训练与预测验证》、《自定义指标》 +- 自动微分（对应现有的《自动微分机制介绍》） +- 转静态图（对应现有的《动态图与静态图一栏》） +- 模型的保存与加载（对应现有的《模型的保存于加载》） +- 分布式训练（对应现有的《单机多卡训练》、《分布式训练快速开始》、《使用 FleetAPI 进行分布式训练》 + +**Paddle 与 CV** + +对应现有 [计算机视觉](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/image_classification.html) 与 [目标检测 YOLOv3](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3106208) 中等的内容。 + +**Paddle 与 NLP** + +对应现有文档中的 [自然语言处理](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2504863) 与 [持续学习语义理解框架](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2538222)等内容。 + +**Paddle 与语音** + +这一部分 Paddle 尚缺少，可以参考 PyTorch 的文档 https://pytorch.org/tutorials/beginner/audio_io_tutorial.html + +**Paddle 与推荐系统** + +对应现有 [推荐系统](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2200430) 中相关文档。 + +**Paddle 与强化学习** + +对应现有文档 [强化学习](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/reinforcement_learning/index_cn.html) 中相关文档。 + +**自定义扩展 Paddle** + +对应现有文档中 [自定义算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/index_cn.html) 中相关文档。且其中的 “Kernel Primitive API” 中的文章，可以上升一个级别。 + +**框架设计文档** + +现有 Paddle 文档欠缺这类文档，可以参考 Mindspore 的文档补充 https://www.mindspore.cn/docs/zh-CN/r1.7/design/technical_white_paper.html。 + +**Cookbook** + +建议将现有的已经足够丰富程独立产品的文档，分离出去，变为独立产品的文档（如部署相关的文档）。 + +将内容不够丰富，又不方便分类的“其它”类型的文档，做成 cookbook（具体名字可以讨论，这里主要是参考的 Python Cookbook 一书），意味着以解决问题为导向的实践指南。 + +比如现有的《自动混合精度训练》、《梯度剪裁方式介绍》、《飞桨模型量化》、《模型导出 ONNX》 等，可以放在这一个栏目。 + +#### “整体介绍”栏目下的结构问题 + +首先是 [整体介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/index_cn.html) 里的目录结构和内容可以考虑调整。 + +如 [整体介绍/基本概念](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/index_cn.html) 中包括了以下内容： + +- Tensor 概念及介绍 +- Paddle 中的模型与层 +- 广播介绍 +- 自动微分 +- 自动混合精度训练 +- 梯度裁剪 + +这部分应该是面对想要了解飞桨并上手使用的初级用户准备的。所以它定位应该是 “Tutorial”。但是有些地方可能讨论改进： + +- [Paddle 中的模型与层](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/layer_and_model_cn.html) 一节与“升级指南” 里的 [组网方式](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#sequential) 一节有内容重叠。 +- [广播介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/broadcasting_cn.html) 更像是 “Explanation” 类的东西，而且内容比较少，可以考虑放置到 API 文档中，以链接的方式引用就可以了。 +- [自动微分机制介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/autograd_cn.html) 一节的内容与 [PyTorch 的 Tutorial](https://pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html) 相比，在介绍 “飞桨自动微分运行机制” 时感觉内容选题复杂了一点。而在“飞桨中自动微分相关所有的使用方法说明” 一节中，没有继续细分“计算梯度”，“关闭梯度”等小标题，作为 Tutorial 感觉友好度还可以加强 +- [自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html) 和 [梯度剪裁](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/gradient_clip_cn.html) 两节内容感觉归为 Guide 更合理，放在“基本概念”中会有点学习曲线陡增的感觉。 + +其次，在 [升级指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html) 包含了这些内容： + +- 数据处理 +- 组网方式 +- 模型训练 +- 单机多卡启动 +- 模型保存 +- 推理 + +这里面包含了很多“干货”的内容，但是我觉得放在“升级指南”中有“明珠暗投”的风险。因为一般人对“升级指南”的刻板印象，应该类似是“relase log”级别的东西，可能会不会优先点开。 + +据我体验，升级指南中的 [使用高层 API](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#id4) 和 [使用基础 API](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#id5) 包含了其它地方没有的内容，这些内容被用户错过的话，是可惜的。 + +而升级指南中的 [单机多卡启动](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#danjiduokaqidong) 和 [分布式训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/index_cn.html) 中的内容非常接近，感觉可以考虑合并。 + +我建议可以对整体介绍中的内容进行重新组织，主要方向有： + +- “整体介绍部分”尽量瘦身，主要以“信息索引”存在 +- 将 “Tensor 概念介绍”、“模型与层”以及上文提到的升级指南中的操作性强又基础的内容，考虑与一级目录 [模型开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/index_cn.html) 合并 +- 将自动混合精度、梯度剪裁以及分布式训练有关的内容，整理为专门领域的 Guide，作为独立的一级目录或二级目录存在 + + +#### 其它一级目录 + +除了上文提到的个人看法外，飞桨的一级目录，大部分都内容充实、结构合理。只是对于少数章节，我个人觉得可以再讨论改进。 + +[性能调优](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/performance_improving/index_cn.html) 作为一个一级目录存在，但其中的二级目录只有 [模型量化](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/performance_improving/quantization.html) 一文。这显得有些单薄。不过在现有的目录结构下，这种选择可能已经是最优的了。我还没有具体的改进建议。 + +但是，如果未来考虑增加新栏目的话，感觉可以把增加一个“开发者实践”类型的栏目，其内容可以参考 MindSpore 的 [开发者精华分享](https://bbs.huaweicloud.com/forum/forumdisplay-fid-1076-orderby-lastpost-filter-typeid-typeid-1255sub.html)。将一些任务导向的话题，如量化、profiling 等“杂项”放在其中。 + +其次 [环境变量](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/flags/flags_cn.html) 这种纯粹的 flags 说明，应该归类为 Reference，感觉将它直接放到 API 文档中可能会更合理。 + +#### 与《教程》栏目的大量重叠 + +[教程](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3465990) 一栏下包括了三类个子栏目： + +- 零基础实践深度学习 +- 产业级实践深度学习 +- 飞桨产业实践范例库 + +这些内容在 [PaddlePaddle/docs](https://github.com/PaddlePaddle/docs/) 仓库似乎找不到，有可能独立于 [文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) 团队之外的另一个团队做的。 + +但是从用户的角度看，它们之间有大量的内容重叠，比如： + +- [使用飞桨重写波士顿房价预测任务](https://www.paddlepaddle.org.cn/tutorials/projectdetail/3469365) VS [使用线性回归预测波士顿房价](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/quick_start/linear_regression.html) +- [分布式训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2538251) VS [分布式训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/index_cn.html) +- [外部用户如何贡献代码](https://www.paddlepaddle.org.cn/tutorials/projectdetail/2538259) VS [参与开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/10_contribution/index_cn.html) +- ... + +它们有些是内容上就是同质的，有些是标题上比较接近（但是内容风格不一致）。我建议如果能更加清晰地将他们做“差异化”处理，可能会更好。 + +## 用户角色代入分析 + +### 初级用户 + +初级用户可以通过 [整体介绍](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)、[模型开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/index_cn.html) 上手飞桨，掌握从数据加载、预处理到模型训练、导出到 ONNX 等各项基本技能。 + +### 中级用户 + +中级用户可以通过 [VisualDL 工具](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/03_VisualDL/index_cn.html)、[动态图转静态图](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/03_VisualDL/index_cn.html)、[推理部署](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/05_inference_deployment/index_cn.html)、以及 [应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/index_cn.html)、[分布式训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/index_cn.html) 中的各类信息，掌握实际落地过程中各个领域的遇到的模型调试、性能调优、部署等问题。 + +### 高级用户 + +想为飞桨开源项目增加功能的用户，可以参考 [自定义算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/07_new_op/index_cn.html) 了解如何增加算子，以及 [参与开发](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/10_contribution/index_cn.html) 了解提交 PR 的流程。 + +不过，其它框架，一般会有文档介绍它组件中的设计理念和原理，如 PyTorch 的这篇 [autograd](https://pytorch.org/docs/stable/notes/autograd.html)，OneFlow 的一些 [技术博客](https://zhuanlan.zhihu.com/p/337851255)，以及 MindSpore 的 [设计文档](https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.6/design/technical_white_paper.html) （这个信息量偏薄弱点）。 + +在 Paddle 的文档体系中，好像没有找到类似的内容，这对于想学习 Paddle 底层原理甚至参与开源建设的爱好者，是一种遗憾。希望可以考虑推出。 + +## 报告总结 + +飞桨文档的完备性、质量和组织结构都已经达到了很高的水准，但是在此分析过程中依然发现一些可以讨论、改进的点，包括： + +- 完备性方面：可加强语音、分布式、设计文档方面的内容 +- 组织结构方面：一些二级标题升级为一级标题；与“教程”中重叠内容较多的文章，考虑加强区分标志 +- 行文风格方面：建议对行文形式上做出更显式的要求（如中英文之间的空格），并做一次彻底的摸排 +- 内容方面：Tutorial 类别质量已经很高，Guide 类别的部分文章可能存在“不必要的知识过多”，“实践性不足”，“配套不完善”的问题，期待改进。 + +个人非常期待飞桨能整理开放出设计文档，让更多人可以了解飞桨开源代码背后的原理和巧妙。 diff --git a/docs/eval/images/blanks.png b/docs/eval/images/blanks.png new file mode 100644 index 0000000000000000000000000000000000000000..dc67d0f7133f6ec4acce906f322aa9c0b8751fc9 GIT binary patch literal 26233 zcmeGDc|4Tg8$XVZ6e&xkNVXKoUWBYeUP3~VEo%u`hU~^NBa+IRov}pLFm_`dr0iR^ zF$P17Z5ZodW*Fbm`}6qyK7W7z{(3xc-;Z<7b*}4qUFV$px}Nv_`I){J%Y_>k0001s zj`n|s004a-?fURM1MM&7!23S!ADx$>)*}FXkY|;4bJkH^PaOcLjJKyH!>7BN@ z7XZN8ar#Tw!7Xgo;XN<$ 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b/docs/guides/10_contribution/index_cn.rst @@ -0,0 +1,10 @@ +############ +参与开发 +############ + +.. toctree:: + :maxdepth: 1 + + local_dev_guide_cn.md + submit_pr_guide_cn.md + faq_cn.rst diff --git a/docs/guides/10_contribution/index_en.rst b/docs/guides/10_contribution/index_en.rst new file mode 100644 index 00000000000..aad872200f5 --- /dev/null +++ b/docs/guides/10_contribution/index_en.rst @@ -0,0 +1,9 @@ +################################# +How to contribute codes to Paddle +################################# + +.. toctree:: + :maxdepth: 1 + + local_dev_guide_en.md + submit_pr_guide_en.md diff --git a/docs/guides/10_contribution/rfcs.md b/docs/guides/10_contribution/rfcs.md new file mode 100644 index 00000000000..b7d84ee1099 --- /dev/null +++ b/docs/guides/10_contribution/rfcs.md @@ -0,0 +1,67 @@ +功能名称： (填写任务的名称, 在 Paddle 中实现 Common Subexpression Elimination（公共子表达式删除）的图优化 pass) + +开始日期：（填写提交时候的日期，YYYY-MM-DD） + +RFC PR：[PaddlePaddle#33333]() + +GitHub Issue：[PaddlePaddle#35993](https://github.com/PaddlePaddle/Paddle/issues/35993) + +# 总结 +一段关于该功能的解释。 + +# 动机 +我们为什么要这样做？它支持哪些用例？预期的结果是什么？ + +# 使用指南级别的说明 +解释这个 rfc，就好像它已经包含在 PaddlePaddle 中，而且你正在教一个 PaddlePaddle 用户。 + +这通常意味着。 + +- 引入新的命名概念。 +- 解释该功能能够实现什么（提示：用例子来思考）。 +- 如果适用，提供错误信息样本、废弃警告或迁移指导。 +- 对于新功能的 RFC，这部分应该提供一个以实例为导向的介绍，并具体解释其影响。 + +# 参考文献级别的说明 +这是 RFC 的技术部分。对这个设计进行足够详细的解释，即 + +- 它与其他功能的交互是清楚的。 +- 合理地清楚该功能将如何实现。 +- 通过实例剖析 corner cases。 +- 该部分应该回到上一节给出的例子，并更充分地解释详细的建议如何使这些例子发挥作用。 + +# 缺点 +为什么我们不应该这样做？ + +# 理论依据和替代方案 +为什么这个设计在可能的设计思路中是最好的？ +还有哪些设计被考虑过，不选择这些设计的理由是什么？ +不这样做的影响是什么？ + +# 现有技术 +讨论现有技术，包括好的和坏的，与本 rfc 有关的。可以包括以下几个例子。 + +- 这个功能在其他深度学习框架中是否存在，讨论他们的社区所做的实验？ +- 对于社区建议。这个功能是否由其他社区完成，他们的经验是什么？ +- 对于其他团队来说。我们可以从其他社区所做的事情中吸取什么教训？ +- 论文。是否有任何已发表的论文或帖子来讨论这个问题？如果你有一些相关的论文可以参考，这可以作为一个更详细的理论背景。 +- 如果没有先例，那也没关系--无论你的未来的可能性想法是全新的还是从其他语言改编的，对我们来说都是有趣的。 + +请注意，虽然其他深度学习框架的先例是某种动力，但它本身并不能成为 RFC 的动力。也请考虑到 PaddlePaddle 有意与其他深度学习框架有所区别。 + +# 未解决的问题 + +- 在合并前，你希望通过 RFC 程序解决设计中的哪些部分？ +- 在功能稳定前，你希望通过实现这个功能来解决哪些部分的设计问题？ +- 你认为哪些相关问题超出了本 RFC 的范围，可以在未来独立于本 RFC 的解决方案来解决？ + +# 未来的可能性 +想一想你的 RFC 的自然延伸和演变，以及它将如何全面影响项目。试着把这部分作为一个工具，在你的 RFC 中更全面地考虑与框架所有可能的互动。还要考虑这一切如何与项目和相关的子团队的路线图相适应。 + +这也是一个 "倾倒想法 "的好地方，如果这些想法超出了你所写的 RFC 的范围，但又与之相关。 + +如果你已经尝试过，但想不出任何未来的可能性，你可以简单地说，你什么都想不出来。 + +请注意，在未来可能性部分写下一些东西并不是接受当前或未来 RFC 的理由；这种说明应该放在本篇或后续 RFC 的动机或理由部分。该部分只是提供额外的信息。 + +参考：[tvm/rfc-template](https://github.com/apache/tvm-rfcs/blob/main/0000-template.md)

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z@4zuA)&83iV|77b<-%^ixn9RW2`k3ANxu`d1e^fcl}ER|F#1s_<=wZc;dDzTHQNfr zPLj|NL4gEmXuj2ebzpCLv)>{fUAXTG~2yd*+r`H zt|UGq*Di012|dslDUk6HP=Wb!kPy{N7Pw!bN?y&O9CFDkh|0m*;sHcXDP&ai|LuX- e(^IFXe_zoOM>L(EFpoTDJeB1&动态图转静态图->案例解析 中全部都是动静转化机制的各种 API 的分章节介绍，建议在案例解析最后增加一个完整的实例代码，比如 cifar10 图像分类的动态图转静态图案例，或者把应用实践中的案例链接附在最后，方便读者找寻。有些读者可能想找一个案例，然后找了使用指南的案例解析，发现没有一个完整的案例，正巧这个读者对整个文档不熟悉，没看过应用实践，然后就找不到案例。 + + +- 意见建议（问题汇总） + +1、接口层面，使用指南->动态图转静态图->使用样例 2.2.1 构造 inputSpec 并没有指出这三种方式构造的 InputSpec 的优缺点。 +2、语法层面，对 numpy 的支持性存在一些问题。 +3、报错调试，在使用排错日志的时候，没有报错信息直接定位到错误代码的位置，且报错内容提示太多，对新手不友好。建议直接在报错的时候，报错的最后位置，重复一遍，最重要的报错信息，并提示报错代码所在位置。 +4、文档层面，在使用指南->动态图转静态图->案例解析 中全部都是动静转化机制的各种 API 的分章节介绍，建议在案例解析最后增加一个完整的实例代码。 diff --git "a/docs/eval/\343\200\220Hackathon No.111\343\200\221PR.md" "b/docs/eval/\343\200\220Hackathon No.111\343\200\221PR.md" new file mode 100644 index 00000000000..b5ef9dbf0c7 --- /dev/null +++ "b/docs/eval/\343\200\220Hackathon No.111\343\200\221PR.md" @@ -0,0 +1,858 @@ +# 飞桨动静转换评估-评估报告 + +| 领域 |飞桨动静转换评估 | +| --- | --- | +|提交作者 | 王源 袁闯闯 | +|提交时间 | 2022-05-04 | +|版本号 | V1.0 | +|依赖飞桨版本 | V2.2 | +|文件名 | 【Hackathon No.111】PR.md | + +一个完整的使用动静转换@to_static 导出、可部署的模型完整代码（参考以图搜图） + +以下为 AI Studio 任务链接 (cifar10 动态图转为静态图（完整 demo）) + +AI Studio 任务链接:https://aistudio.baidu.com/aistudio/projectdetail/3937674?channel=0&channelType=0&shared=1 + +# 1、任务描述： + + 飞桨框架于 2.0 正式版之后正式发布了动静转换@to_static 功能，并在 2.1、2.2 两个大版本中不断新增了各项功能，以及详细的使用文档和最佳实践教程（以图搜图）。 + + 在本任务中，我们希望你全面体验飞桨的动静转换@to_static 功能，即参考飞桨官网 -> 使用指南 -> 动态图转静态 下的内容，体验动转静模型导出、动转静训练等功能， + + 产出一份整体功能体验报告。 + +# 2、环境配置： + + 因为需要体验飞桨 paddlepaddle 框架的动转静模型导出、动转静训练等功能，所以首先需要安装飞桨 paddlepaddle 框架，运行环境使用 pycharm 和 anaconda。 + + 所以在进行 PaddlePaddle 安装之前应确保 Anaconda 软件环境已经正确安装。软件下载和安装参见 Anaconda 官网(https://www.anaconda.com/)。 + + 在已经正确安装 Anaconda 的情况下请按照下列步骤安装 PaddlePaddle。 + + -Windows 7/8/10 专业版/企业版 (64bit) + + -GPU 版本支持 CUDA 10.1/10.2/11.2，且仅支持单卡 + + -conda 版本 4.8.3+ (64 bit) + +## 2.1、创建虚拟环境： + +- 1、安装环境 + 首先根据具体的 Python 版本创建 Anaconda 虚拟环境，PaddlePaddle 的 Anaconda 安装支持以下四种 Python 安装环境。 +如果您想使用的 python 版本为 3.6: +```python +conda create -n paddle_env python=3.6 +``` +如果您想使用的 python 版本为 3.7: +```python +conda create -n paddle_env python=3.7 +``` +如果您想使用的 python 版本为 3.8: +```python +conda create -n paddle_env python=3.8 +``` +如果您想使用的 python 版本为 3.9: +```python +conda create -n paddle_env python=3.9 +``` +本实验使用 python 版本为 3.9,虚拟环境命名为 paddlepaddle-gpu，即： +```python +conda create -n paddlepaddle-gpu python=3.9 +``` +- 2、进入 Anaconda 虚拟环境 +```python +activate paddlepaddle-gpu +``` +- 3、开始安装 +GPU 版的 PaddlePaddle +本实验为 CUDA 10.2，需要搭配 cuDNN 7 (cuDNN>=7.6.5, 多卡环境下 NCCL>=2.7) +添加清华源（可选），对于国内用户无法连接到 Anaconda 官方源的可以按照以下命令添加清华源: +```python +conda install paddlepaddle-gpu==2.2.2 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ +``` +您可参考 NVIDIA 官方文档了解 CUDA 和 CUDNN 的安装流程和配置方法。 +- 4、验证安装 +安装完成后您可以使用 python 或 python3 进入 python 解释器，输入 import paddle ，再输入 paddle.utils.run_check() + +如果出现 PaddlePaddle is installed successfully!，说明您已成功安装。 + +## 2.2、paddlepaddle 环境配置： +```python +import paddle +import paddle.nn.functional as F +import numpy as np +import random +import matplotlib.pyplot as plt +from PIL import Image +from collections import defaultdict + +print(paddle.__version__) +``` +```python +2.2.2 +``` + +# 3、数据加载： + +## 3.1、数据集介绍 + +本示例采用 CIFAR-10 数据集。 + +CIFAR-10 和 CIFAR-100 是 8000 万个微小图像数据集的标记子集。它们由 Alex Krizhevsky、Vinod Nair 和 Geoffrey Hinton 收集。 + +数据集分为五个训练批次和一个测试批次，每个批次有 10000 张图像。测试批次恰好包含来自每个类别的 1000 个随机选择的图像。训练批次包含随机顺序的剩余图像， + +但一些训练批次可能包含来自一个类的图像多于另一个。在它们之间，训练批次恰好包含来自每个类别的 5000 张图像。 + +以下是数据集中的类： + + + 飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船、卡车 + + +这些类是完全互斥的。汽车和卡车之间没有重叠。“汽车”包括轿车、SUV 之类的东西。“卡车”只包括大卡车。两者都不包括皮卡车。 + +数据集下载网址为：https://www.cs.toronto.edu/~kriz/cifar.html +```python +import paddle.vision.transforms as T + +transform = T.Compose([T.Transpose((2, 0, 1))]) + +cifar10_train = paddle.vision.datasets.Cifar10(mode='train', transform=transform) +x_train = np.zeros((50000, 3, 32, 32)) +y_train = np.zeros((50000, 1), dtype='int32') + +for i in range(len(cifar10_train)): + train_image, train_label = cifar10_train[i] + + # normalize the data + x_train[i,:, :, :] = train_image / 255. + y_train[i, 0] = train_label + +y_train = np.squeeze(y_train) + +cifar10_test = paddle.vision.datasets.cifar.Cifar10(mode='test', transform=transform) +x_test = np.zeros((10000, 3, 32, 32), dtype='float32') +y_test = np.zeros((10000, 1), dtype='int64') + +for i in range(len(cifar10_test)): + test_image, test_label = cifar10_test[i] + + # normalize the data + x_test[i,:, :, :] = test_image / 255. + y_test[i, 0] = test_label + +y_test = np.squeeze(y_test) + +height_width = 32 + +def show_collage(examples): + box_size = height_width + 2 + num_rows, num_cols = examples.shape[:2] + + collage = Image.new( + mode="RGB", + size=(num_cols * box_size, num_rows * box_size), + color=(255, 255, 255), + ) + for row_idx in range(num_rows): + for col_idx in range(num_cols): + array = (np.array(examples[row_idx, col_idx]) * 255).astype(np.uint8) + array = array.transpose(1,2,0) + collage.paste( + Image.fromarray(array), (col_idx * box_size, row_idx * box_size) + ) + + collage = collage.resize((2 * num_cols * box_size, 2 * num_rows * box_size)) + return collage + +sample_idxs = np.random.randint(0, 50000, size=(5, 5)) +examples = x_train[sample_idxs] +show_collage(examples) +``` +## 3.2、构建训练数据 + +图片检索的模型的训练样本跟常见的分类任务的训练样本不太一样的地方在于，每个训练样本并不是一个(image, class)这样的形式。而是（image0, image1, similary_or_not)的形式，即，每 + +一个训练样本由两张图片组成，而其 label 是这两张图片是否相似的标志位（0 或者 1）。 + +很自然的能够想到，来自同一个类别的两张图片，是相似的图片，而来自不同类别的两张图片，应该是不相似的图片。 + +为了能够方便的抽样出相似图片（以及不相似图片）的样本，先建立能够根据类别找到该类别下所有图片的索引。 +```python +class_idx_to_train_idxs = defaultdict(list) +for y_train_idx, y in enumerate(y_train): + class_idx_to_train_idxs[y].append(y_train_idx) + +class_idx_to_test_idxs = defaultdict(list) +for y_test_idx, y in enumerate(y_test): + class_idx_to_test_idxs[y].append(y_test_idx) + +num_classes = 10 + +def reader_creator(num_batchs): + def reader(): + iter_step = 0 + while True: + if iter_step >= num_batchs: + break + iter_step += 1 + x = np.empty((2, num_classes, 3, height_width, height_width), dtype=np.float32) + for class_idx in range(num_classes): + examples_for_class = class_idx_to_train_idxs[class_idx] + anchor_idx = random.choice(examples_for_class) + positive_idx = random.choice(examples_for_class) + while positive_idx == anchor_idx: + positive_idx = random.choice(examples_for_class) + x[0, class_idx] = x_train[anchor_idx] + x[1, class_idx] = x_train[positive_idx] + yield x + + return reader + +def anchor_positive_pairs(num_batchs=100): + return reader_creator(num_batchs) + +pairs_train_reader = anchor_positive_pairs(num_batchs=1000) +``` +# 4、模型组网： +把图片转换为高维的向量表示的网络 +目标是首先把图片转换为高维空间的表示，然后计算图片在高维空间表示时的相似度。 下面的网络结构用来把一个形状为(3, 32, 32)的图片转换成形状为(8,)的向量。在有些资料中也会把这个转换成的向量称为 Embedding，请注意，这与自然语言处理领域的词向量的区别。 下面的模型由三个连续的卷积加一个全局均值池化，然后用一个线性全链接层映射到维数为 8 的向量空间。为了后续计算余弦相似度时的便利，还在最后做了归一化。（即，余弦相似度的分母部分） +```python +class MyNet(paddle.nn.Layer): + def __init__(self): + super(MyNet, self).__init__() + + self.conv1 = paddle.nn.Conv2D(in_channels=3, + out_channels=32, + kernel_size=(3, 3), + stride=2) + + self.conv2 = paddle.nn.Conv2D(in_channels=32, + out_channels=64, + kernel_size=(3,3), + stride=2) + + self.conv3 = paddle.nn.Conv2D(in_channels=64, + out_channels=128, + kernel_size=(3,3), + stride=2) + + self.gloabl_pool = paddle.nn.AdaptiveAvgPool2D((1,1)) + + self.fc1 = paddle.nn.Linear(in_features=128, out_features=8) + + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.conv2(x) + x = F.relu(x) + x = self.conv3(x) + x = F.relu(x) + x = self.gloabl_pool(x) + x = paddle.squeeze(x, axis=[2, 3]) + x = self.fc1(x) + x = x / paddle.norm(x, axis=1, keepdim=True) + return x +``` +# 5、模型训练： + +将 epoch 设置为 10，进行模型训练 + +```python +def train(model): + print('start training ... ') + model.train() + + inverse_temperature = paddle.to_tensor(np.array([1.0/0.2], dtype='float32')) + + epoch_num = 10 + + opt = paddle.optimizer.Adam(learning_rate=0.0001, + parameters=model.parameters()) + + for epoch in range(epoch_num): + for batch_id, data in enumerate(pairs_train_reader()): + anchors_data, positives_data = data[0], data[1] + + anchors = paddle.to_tensor(anchors_data) + positives = paddle.to_tensor(positives_data) + + anchor_embeddings = model(anchors) + positive_embeddings = model(positives) + + similarities = paddle.matmul(anchor_embeddings, positive_embeddings, transpose_y=True) + similarities = paddle.multiply(similarities, inverse_temperature) + + sparse_labels = paddle.arange(0, num_classes, dtype='int64') + + loss = F.cross_entropy(similarities, sparse_labels) + + if batch_id % 500 == 0: + print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy())) + loss.backward() + opt.step() + opt.clear_grad() + +model = MyNet() +train(model) +``` + +```python +start training ... +epoch: 0, batch_id: 0, loss is: [2.2915785] +epoch: 0, batch_id: 500, loss is: [2.4421422] +epoch: 0, batch_id: 1000, loss is: [1.7860969] +epoch: 0, batch_id: 1500, loss is: [2.3819976] +epoch: 1, batch_id: 0, loss is: [2.2505655] +epoch: 1, batch_id: 500, loss is: [1.6781666] +epoch: 1, batch_id: 1000, loss is: [1.8037045] +epoch: 1, batch_id: 1500, loss is: [1.8967863] +epoch: 2, batch_id: 0, loss is: [1.775666] +epoch: 2, batch_id: 500, loss is: [2.0136874] +epoch: 2, batch_id: 1000, loss is: [2.103913] +epoch: 2, batch_id: 1500, loss is: [1.7592652] +epoch: 3, batch_id: 0, loss is: [1.7325624] +epoch: 3, batch_id: 500, loss is: [1.9885247] +epoch: 3, batch_id: 1000, loss is: [2.3454063] +epoch: 3, batch_id: 1500, loss is: [1.9360502] +epoch: 4, batch_id: 0, loss is: [2.1657584] +epoch: 4, batch_id: 500, loss is: [2.0958445] +epoch: 4, batch_id: 1000, loss is: [1.9509046] +epoch: 4, batch_id: 1500, loss is: [1.8858738] +epoch: 5, batch_id: 0, loss is: [1.9648739] +epoch: 5, batch_id: 500, loss is: [1.8831095] +epoch: 5, batch_id: 1000, loss is: [1.9274123] +epoch: 5, batch_id: 1500, loss is: [2.2648232] +epoch: 6, batch_id: 0, loss is: [2.131785] +epoch: 6, batch_id: 500, loss is: [1.7363421] +epoch: 6, batch_id: 1000, loss is: [2.2151723] +epoch: 6, batch_id: 1500, loss is: [1.5245721] +epoch: 7, batch_id: 0, loss is: [1.7423642] +epoch: 7, batch_id: 500, loss is: [1.5562365] +epoch: 7, batch_id: 1000, loss is: [1.6524445] +epoch: 7, batch_id: 1500, loss is: [1.9120047] +epoch: 8, batch_id: 0, loss is: [1.8247225] +epoch: 8, batch_id: 500, loss is: [1.5704175] +epoch: 8, batch_id: 1000, loss is: [1.9273182] +epoch: 8, batch_id: 1500, loss is: [1.7724463] +epoch: 9, batch_id: 0, loss is: [1.5964721] +epoch: 9, batch_id: 500, loss is: [1.5145239] +epoch: 9, batch_id: 1000, loss is: [1.8208185] +epoch: 9, batch_id: 1500, loss is: [2.3465972] + +``` +# 6、模型预测： +前述的模型训练训练结束之后，就可以用该网络结构来计算出任意一张图片的高维向量表示（embedding)，通过计算该图片与图片库中其他图片的高维向量表示之间的相似度， +就可以按照相似程度进行排序，排序越靠前，则相似程度越高。 + +下面对测试集中所有的图片都两两计算相似度，然后选一部分相似的图片展示出来。 + +```python +near_neighbours_per_example = 10 + +x_test_t = paddle.to_tensor(x_test) +test_images_embeddings = model(x_test_t) +similarities_matrix = paddle.matmul(test_images_embeddings, test_images_embeddings, transpose_y=True) + +indicies = paddle.argsort(similarities_matrix, descending=True) +indicies = indicies.numpy() + +examples = np.empty( + ( + num_classes, + near_neighbours_per_example + 1, + 3, + height_width, + height_width, + ), + dtype=np.float32, +) + +for row_idx in range(num_classes): + examples_for_class = class_idx_to_test_idxs[row_idx] + anchor_idx = random.choice(examples_for_class) + + examples[row_idx, 0] = x_test[anchor_idx] + anchor_near_neighbours = indicies[anchor_idx][1:near_neighbours_per_example+1] + for col_idx, nn_idx in enumerate(anchor_near_neighbours): + examples[row_idx, col_idx + 1] = x_test[nn_idx] + +show_collage(examples) +``` +# 7、使用 @to_static 进行动静转换： +动静转换（@to_static）通过解析 Python 代码（抽象语法树，下简称：AST） 实现一行代码即可将动态图转为静态图的功能，只需在待转化的函数前添加一个装饰器 @paddle.jit.to_static + +使用 @to_static 即支持 可训练可部署 ，也支持只部署（详见模型导出） ，常见使用方式如下： + +方式一：使用 @to_static 装饰器装饰 SimpleNet (继承了 nn.Layer) 的 forward 函数: +```python +import paddle +from paddle.jit import to_static + +class SimpleNet(paddle.nn.Layer): + def __init__(self): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + + @to_static # 动静转换 + def forward(self, x, y): + out = self.linear(x) + out = out + y + return out + +net = SimpleNet() +net.eval() +x = paddle.rand([2, 10]) +y = paddle.rand([2, 3]) +out = net(x, y) # 动转静训练 +paddle.jit.save(net, './net') # 导出预测模型 +``` +方式二：调用 paddle.jit.to_static() 函数，仅做预测模型导出时推荐此种用法。 +```python +import paddle +from paddle.jit import to_static + +class SimpleNet(paddle.nn.Layer): + def __init__(self): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + + def forward(self, x, y): + out = self.linear(x) + out = out + y + return out + +net = SimpleNet() +net.eval() +net = paddle.jit.to_static(net) # 动静转换 +x = paddle.rand([2, 10]) +y = paddle.rand([2, 3]) +out = net(x, y) # 动转静训练 +paddle.jit.save(net, './net') # 导出预测模型 + +``` +方式一和方式二的主要区别是，前者直接在 forward() 函数定义处装饰，后者显式调用了 jit.to_static()方法，默认会对 net.forward 进行动静转换。 + +本实验使用 paddle.jit.to_static 实现动转静： +飞桨推荐使用 @paddle.jit.to_static 实现动转静，也被称为基于源代码转写的动态图转静态图，其基本原理是通过分析 Python 代码来将动态图代码转写为静态图代码，并在底层自动使用执行器运行，使用起来非常方便，只需要在原网络结构的 forward 前添加一个装饰器 paddle.jit.to_static 即可。 + +## 7.1、改写组网代码 +```python +class MyNet2(paddle.nn.Layer): + def __init__(self): + super(MyNet2, self).__init__() + + self.conv1 = paddle.nn.Conv2D(in_channels=3, + out_channels=32, + kernel_size=(3, 3), + stride=2) + + self.conv2 = paddle.nn.Conv2D(in_channels=32, + out_channels=64, + kernel_size=(3,3), + stride=2) + + self.conv3 = paddle.nn.Conv2D(in_channels=64, + out_channels=128, + kernel_size=(3,3), + stride=2) + + self.gloabl_pool = paddle.nn.AdaptiveAvgPool2D((1,1)) + + self.fc1 = paddle.nn.Linear(in_features=128, out_features=8) + + # 在 forward 前添加 paddle.jit.to_static 装饰器 + @paddle.jit.to_static() + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.conv2(x) + x = F.relu(x) + x = self.conv3(x) + x = F.relu(x) + x = self.gloabl_pool(x) + x = paddle.squeeze(x, axis=[2, 3]) + x = self.fc1(x) + x = x / paddle.norm(x, axis=1, keepdim=True) + return x +``` +通过 model.summary 查看网络结构。 +```python +model_2 = MyNet2() +model_info = paddle.summary(model_2, (10, 3, 32, 32)) +print(model_info) +``` + +```python +------------------------------------------------------------------------------- + Layer (type) Input Shape Output Shape Param # +=============================================================================== + Conv2D-4 [[10, 3, 32, 32]] [10, 32, 15, 15] 896 + Conv2D-5 [[10, 32, 15, 15]] [10, 64, 7, 7] 18,496 + Conv2D-6 [[10, 64, 7, 7]] [10, 128, 3, 3] 73,856 +AdaptiveAvgPool2D-2 [[10, 128, 3, 3]] [10, 128, 1, 1] 0 + Linear-2 [[10, 128]] [10, 8] 1,032 +=============================================================================== +Total params: 94,280 +Trainable params: 94,280 +Non-trainable params: 0 +------------------------------------------------------------------------------- +Input size (MB): 0.12 +Forward/backward pass size (MB): 0.89 +Params size (MB): 0.36 +Estimated Total Size (MB): 1.36 +------------------------------------------------------------------------------- + + +{'total_params': 94280, 'trainable_params': 94280} + +``` + +## 7.2、模型训练 +使用 paddle.jit.to_static 装饰器后，训练方式仍与原动态图训练一致。因此这里直接传入 model_2 完成模型的训练。 +```python +train(model_2) +``` + +```python +start training ... +epoch: 0, batch_id: 0, loss is: [2.2707999] +epoch: 0, batch_id: 500, loss is: [2.2578232] +epoch: 0, batch_id: 1000, loss is: [2.0026908] +epoch: 0, batch_id: 1500, loss is: [2.1243637] +epoch: 1, batch_id: 0, loss is: [2.5174978] +epoch: 1, batch_id: 500, loss is: [1.9235588] +epoch: 1, batch_id: 1000, loss is: [2.241912] +epoch: 1, batch_id: 1500, loss is: [2.2027836] +epoch: 2, batch_id: 0, loss is: [2.0674071] +epoch: 2, batch_id: 500, loss is: [1.8517029] +epoch: 2, batch_id: 1000, loss is: [1.9565346] +epoch: 2, batch_id: 1500, loss is: [2.2468033] +epoch: 3, batch_id: 0, loss is: [1.5385025] +epoch: 3, batch_id: 500, loss is: [2.1791337] +epoch: 3, batch_id: 1000, loss is: [2.0335] +epoch: 3, batch_id: 1500, loss is: [1.8313652] +epoch: 4, batch_id: 0, loss is: [1.8956888] +epoch: 4, batch_id: 500, loss is: [1.6906776] +epoch: 4, batch_id: 1000, loss is: [2.0118344] +epoch: 4, batch_id: 1500, loss is: [2.002913] +epoch: 5, batch_id: 0, loss is: [1.8000762] +epoch: 5, batch_id: 500, loss is: [1.7253144] +epoch: 5, batch_id: 1000, loss is: [1.5976737] +epoch: 5, batch_id: 1500, loss is: [1.5003413] +epoch: 6, batch_id: 0, loss is: [1.8904054] +epoch: 6, batch_id: 500, loss is: [2.1880364] +epoch: 6, batch_id: 1000, loss is: [2.0464098] +epoch: 6, batch_id: 1500, loss is: [1.7705017] +epoch: 7, batch_id: 0, loss is: [1.8255459] +epoch: 7, batch_id: 500, loss is: [1.8008741] +epoch: 7, batch_id: 1000, loss is: [1.9753224] +epoch: 7, batch_id: 1500, loss is: [2.376344] +epoch: 8, batch_id: 0, loss is: [1.7606968] +epoch: 8, batch_id: 500, loss is: [1.9435362] +epoch: 8, batch_id: 1000, loss is: [2.3564293] +epoch: 8, batch_id: 1500, loss is: [1.9458401] +epoch: 9, batch_id: 0, loss is: [1.7150522] +epoch: 9, batch_id: 500, loss is: [1.9844353] +epoch: 9, batch_id: 1000, loss is: [1.962418] +epoch: 9, batch_id: 1500, loss is: [1.7263882] + +``` +## 7.3、动转静模型导出 +动转静模块是架在动态图与静态图的一个桥梁，旨在打破动态图模型训练与静态部署的鸿沟，消除部署时对模型代码的依赖，打通与预测端的交互逻辑。 +在处理逻辑上，动转静主要包含两个主要模块： + +代码层面：将模型中所有的 layers 接口在静态图模式下执行以转为 Op ，从而生成完整的静态 Program + +Tensor 层面：将所有的 Parameters 和 Buffers 转为可导出的 Variable 参数（ persistable=True ） +通过 forward 导出预测模型 +通过 forward 导出预测模型导出一般包括三个步骤： + +切换 eval() 模式：类似 Dropout 、LayerNorm 等接口在 train() 和 eval() 的行为存在较大的差异，在模型导出前，请务必确认模型已切换到正确的模式，否则导出的模型在预测阶段可能出现输出结果不符合预期的情况。 + +构造 InputSpec 信息：InputSpec 用于表示输入的 shape、dtype、name 信息，且支持用 None 表示动态 shape（如输入的 batch_size 维度），是辅助动静转换的必要描述信息。 + +调用 save 接口：调用 paddle.jit.save 接口，若传入的参数是类实例，则默认对 forward 函数进行 @to_static 装饰，并导出其对应的模型文件和参数文件。 + +如下是一个简单的示例： + +```python +import paddle +from paddle.jit import to_static +from paddle.static import InputSpec + +class SimpleNet(paddle.nn.Layer): + def __init__(self): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + + def forward(self, x, y): + out = self.linear(x) + out = out + y + return out + + def another_func(self, x): + out = self.linear(x) + out = out * 2 + return out + +net = SimpleNet() +# train(net) 模型训练 (略) + +# step 1: 切换到 eval() 模式 +net.eval() + +# step 2: 定义 InputSpec 信息 +x_spec = InputSpec(shape=[None, 3], dtype='float32', name='x') +y_spec = InputSpec(shape=[3], dtype='float32', name='y') + +# step 3: 调用 jit.save 接口 +net = paddle.jit.save(net, path='simple_net', input_spec=[x_spec, y_spec]) # 动静转换 + +``` +本实验使用 paddle.jit.save 保存动转静模型 +使用 paddle.jit.to_static 转换模型后，需要调用 paddle.jit.save 将保存模型，以供后续的预测部署。保存后，会产生 model.pdmodel 、model.pdiparams.info、model.pdiparams 三个文件。 +```python +paddle.jit.save(model_2, 'model') +``` +使用 InputSpec 指定模型输入 Tensor 信息 +动静转换在生成静态图 Program 时，依赖输入 Tensor 的 shape、dtype 和 name 信息。因此，Paddle 提供了 InputSpec 接口，用于指定输入 Tensor 的描述信息，并支持动态 shape 特性。 + +构造 InputSpec +方式一：直接构造 + +InputSpec 接口在 paddle.static 目录下， 只有 shape 是必须参数， dtype 和 name 可以缺省，默认取值分别为 float32 和 None 。使用样例如下： +```python +from paddle.static import InputSpec + +x = InputSpec([None, 784], 'float32', 'x') +label = InputSpec([None, 1], 'int64', 'label') + +print(x) # InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x) +print(label) # InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label) + +``` +方式二：由 Tensor 构造 + +可以借助 InputSpec.from_tensor 方法，从一个 Tensor 直接创建 InputSpec 对象，其拥有与源 Tensor 相同的 shape 和 dtype 。 使用样例如下： +```python +import numpy as np +import paddle +from paddle.static import InputSpec + +x = paddle.to_tensor(np.ones([2, 2], np.float32)) +x_spec = InputSpec.from_tensor(x, name='x') +print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x) + +``` +注：若未在 from_tensor 中指定新的 name，则默认使用与源 Tensor 相同的 name。 + +方式三：由 numpy.ndarray + +也可以借助 InputSpec.from_numpy 方法，从一个 Numpy.ndarray 直接创建 InputSpec 对象，其拥有与源 ndarray 相同的 shape 和 dtype 。使用样例如下： +```python +import numpy as np +from paddle.static import InputSpec + +x = np.ones([2, 2], np.float32) +x_spec = InputSpec.from_numpy(x, name='x') +print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x) + +``` +注：若未在 from_numpy 中指定新的 name，则默认使用 None 。 +基本用法 +方式一： 在 @to_static 装饰器中调用 + +如下是一个简单的使用样例： + +```python +import paddle +from paddle.nn import Layer +from paddle.jit import to_static +from paddle.static import InputSpec + +class SimpleNet(Layer): + def __init__(self): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + + @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]) + def forward(self, x, y): + out = self.linear(x) + out = out + y + return out + +net = SimpleNet() + +# save static graph model for inference directly +paddle.jit.save(net, './simple_net') + +``` +在上述的样例中， @to_static 装饰器中的 input_spec 为一个 InputSpec 对象组成的列表，用于依次指定参数 x 和 y 对应的 Tensor 签名信息。在实例化 SimpleNet 后，可以直接调用 paddle.jit.save 保存静态图模型，不需要执行任何其他的代码。 + +注： + +input_spec 参数中不仅支持 InputSpec 对象，也支持 int 、 float 等常见 Python 原生类型。 + +若指定 input_spec 参数，则需为被装饰函数的所有必选参数都添加对应的 InputSpec 对象，如上述样例中，不支持仅指定 x 的签名信息。 + +若被装饰函数中包括非 Tensor 参数，推荐函数的非 Tensor 参数设置默认值，如 forward(self, x, use_bn=False) + +方式二：在 to_static 函数中调用 + +若期望在动态图下训练模型，在训练完成后保存预测模型，并指定预测时需要的签名信息，则可以选择在保存模型时，直接调用 to_static 函数。使用样例如下： +```python +class SimpleNet(Layer): + def __init__(self): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + + def forward(self, x, y): + out = self.linear(x) + out = out + y + return out + +net = SimpleNet() + +# train process (Pseudo code) +for epoch_id in range(10): + train_step(net, train_reader) + +net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]) + +# save static graph model for inference directly +paddle.jit.save(net, './simple_net') + +``` +如上述样例代码中，在完成训练后，可以借助 to_static(net, input_spec=...) 形式对模型实例进行处理。Paddle 会根据 input_spec 信息对 forward 函数进行递归的动转静，得到完整的静态图，且包括当前训练好的参数数据。 + +方式三：通过 list 和 dict 推导 + +上述两个样例中，被装饰的 forward 函数的参数均为 Tensor 。这种情况下，参数个数必须与 InputSpec 个数相同。但当被装饰的函数参数为 list 或 dict 类型时，input_spec 需要与函数参数保持相同的嵌套结构。 + +当函数的参数为 list 类型时，input_spec 列表中对应元素的位置，也必须是包含相同元素的 InputSpec 列表。使用样例如下： +```python +class SimpleNet(Layer): + def __init__(self): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + + @to_static(input_spec=[[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]]) + def forward(self, inputs): + x, y = inputs[0], inputs[1] + out = self.linear(x) + out = out + y + return out + +``` +其中 input_spec 参数是长度为 2 的 list ，对应 forward 函数的 x 和 bias_info 两个参数。 input_spec 的最后一个元素是包含键名为 x 的 InputSpec 对象的 dict ，对应参数 bias_info 的 Tensor 签名信息。 + +方式四：指定非 Tensor 参数类型 + +目前，to_static 装饰器中的 input_spec 参数仅接收 InputSpec 类型对象。若被装饰函数的参数列表除了 Tensor 类型，还包含其他如 Int、 String 等非 Tensor 类型时，推荐在函数中使用 kwargs 形式定义非 Tensor 参数，如下述样例中的 use_act 参数。 + + +```python + +class SimpleNet(Layer): + def __init__(self, ): + super(SimpleNet, self).__init__() + self.linear = paddle.nn.Linear(10, 3) + self.relu = paddle.nn.ReLU() + + def forward(self, x, use_act=False): + out = self.linear(x) + if use_act: + out = self.relu(out) + return out + +net = SimpleNet() +# 方式一：save inference model with use_act=False +net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x')]) +paddle.jit.save(net, path='./simple_net') + + +# 方式二：save inference model with use_act=True +net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x'), True]) +paddle.jit.save(net, path='./simple_net') + +``` +在上述样例中，假设 step 为奇数时，use_act 取值为 False ； step 为偶数时， use_act 取值为 True 。动转静支持非 Tensor 参数在训练时取不同的值，且保证了取值不同的训练过程都可以更新模型的网络参数，行为与动态图一致。 + +在借助 paddle.jit.save 保存预测模型时，动转静会根据 input_spec 和 kwargs 的默认值保存推理模型和网络参数。建议将 kwargs 参数默认值设置为预测时的取值。 + +## 7.4、使用 paddle.jit.load 加载动转静模型 +将模型导出后，需要使用 paddle.jit.load 加载模型。加载后的模型可以直接用于预测。 +```python +model_2 = paddle.jit.load('model') +``` +## 7.5、使用动转静模型 +前述的模型训练训练结束之后，就可以用该网络结构来计算出任意一张图片的高维向量表示（embedding)，通过计算该图片与图片库中其他图片的高维向量表示之间的相似度，就可以按照相似程度进行排序，排序越靠前，则相似程度越高。 + +下面对测试集中所有的图片都两两计算相似度，然后选一部分相似的图片展示出来。 +```python +near_neighbours_per_example = 10 + +x_test_t = paddle.to_tensor(x_test) +test_images_embeddings = model_2(x_test_t) +similarities_matrix = paddle.matmul(test_images_embeddings, test_images_embeddings, transpose_y=True) + +indicies = paddle.argsort(similarities_matrix, descending=True) +indicies = indicies.numpy() + +examples = np.empty( + ( + num_classes, + near_neighbours_per_example + 1, + 3, + height_width, + height_width, + ), + dtype=np.float32, +) + +for row_idx in range(num_classes): + examples_for_class = class_idx_to_test_idxs[row_idx] + anchor_idx = random.choice(examples_for_class) + + examples[row_idx, 0] = x_test[anchor_idx] + anchor_near_neighbours = indicies[anchor_idx][1:near_neighbours_per_example+1] + for col_idx, nn_idx in enumerate(anchor_near_neighbours): + examples[row_idx, col_idx + 1] = x_test[nn_idx] + +show_collage(examples) +``` +通过上述的内容，就使用 @jit.to_static 完成了动转静并使用该模型进行了预测。 +# 8、 总结： +上述动态图转静态图的过程中，总体来说感觉还是可以的，具体总结了以下几个层面： + +- 1、接口层面： + 接口功能目前使用中覆盖了所有使用场景；InputSpec 指定信息也好用 +- 2、语法层面： + 语法支持方面尚未发现问题，我认为语法支持比较完备，但是图片展示功能（show_collage(examples)）在运行时展示不出来；控制流语法转换比较流畅 +- 3、报错层面： + 报错信息可读性差比较好，比如： + Traceback (most recent call last): + File "D:\Postgraduate\deep_learning\jiaoliu\pp\train.py", line 191, in + indicies = paddle.argsort(similarities_matrix, descending=True) + File "F:\Users\ASUS\anaconda3\envs\paddlepaddle-gpu\lib\site-packages\paddle\tensor\search.py", line 92, in argsort + _, ids = _C_ops.argsort(x, 'axis', axis, 'descending', descending) + SystemError: (Fatal) Operator argsort raises an struct paddle::memory::allocation::BadAlloc exception. + The exception content is + :ResourceExhaustedError: + + Out of memory error on GPU 0. Cannot allocate 762.939697MB memory on GPU 0, 3.256321GB memory has been allocated and available memory is only 761.527736MB. + + Please check whether there is any other process using GPU 0. + 1. If yes, please stop them, or start PaddlePaddle on another GPU. + 2. If no, please decrease the batch size of your model. + 但如果没有详细的提示信息还需上网查找解决方案；调试工具比较易用 +- 4、文档层面： + 从官方文档来说，动态图转静态图的示例文档感觉不太完善，例如 paddle.jit.load 等的 API 没有在使用指南的示例文档中展现，教程文档还有待完善，其他方面感觉内容比较详细丰富，具有较好的指导性 +- 5、意见建议： + 动态图转静态图的代码还是比较方便的，但对新手来说有一定难度，建议在使用指南中可以适当增加一些重点难点视频解说； + 有一些官网上的使用指南用于学习，但是有一些是在其使用指南中没有介绍的，例如数据集的拆分等这些需要自己去琢磨，可再丰富一下指南内容； + 使用指南中的方式或方法比较多的可以用序号组合一下，方便学习者更快的了解每种方式或方法的优缺点; + paddle 自带数据集太大，数据处理需消耗大量资源，使用起来不太方便，建议新增一些小的数据集。 diff --git "a/docs/eval/\343\200\220Hackathon No.112\343\200\221 PR.md" "b/docs/eval/\343\200\220Hackathon No.112\343\200\221 PR.md" new file mode 100644 index 00000000000..2200ceb82c1 --- /dev/null +++ "b/docs/eval/\343\200\220Hackathon No.112\343\200\221 PR.md" @@ -0,0 +1,702 @@ +# 深度体验飞桨分布式训练功能，并产出一份评估报告 + +| | | +| ------------ | ------------------------------------------------- | +| 提交作者 | mkm wjc | +| 提交时间 | 2022-04-02 | +| 版本号 | V1.0 | +| 依赖飞桨版本 | develop | +| 文件名 | 【Hackathon No.112】 PR.md | + +# 功能对比表格 + +| 功能对比表格 | | | | +| ------------ | ------------------------------------------------- | ------------------------------ | -------------------- | +| 类型 | 选项（选择一项） | 您的选择 | 使用时遇到的问题或建议 | +| 任务模式 | 单机多卡 or 多机多卡 | 单机多卡 | - | +| 运行方式 | 物理机 or docker or AI 开发平台 | docker | - | +| 任务类型 | 集合通信（GPU）or 参数服务器（CPU/GPU） | 集合通信（GPU） | - | +| 分布式训练使用程度 | 成功实践过 or 熟悉基本过程 or 了解基本过程 or 不熟悉 | 成功实践过 | 在使用曙光平台 docker 创建镜像后难以使用多节点进行分布式训练，建议推出一种可以直接在 E-Shell 安装的方式，可以使得任务通过队列的方式提交 | +| 分布式策略使用程度 | 使用过调优策略 or 了解部分优化策略 or 没有使用过优化策略 | 了解部分优化策略 | 无 | +| 分布式数据处理使用程度 | 熟悉 or 了解 or 不清楚 | 熟悉 | 无 | +| 分布式模型保存和加载使用程度 | 熟悉 or 了解 or 不清楚 | 熟悉 | 无 | +| 遇到问题如何解决（可多选） | 日志排查 or 社区反馈 or 提 Issue or 其他 | 日志排查、社区反馈 | 建议在官方文档中对常见问题进行汇总，便于使用者解决问题 | + + +# 一、摘要 + +本评估方案将从以下几个方面对 paddle 分布式框架进行评估： +- 1、环境配置，对比 pytorch 环境以及 paddle 环境在曙光超算上的配置 + 对曙光超算如何使用 paddle 进行分布式计算进行了介绍 + +- 2、Fleet API 的使用，对比 PyTorch API 与 Fleet API 的区别 + +- 3、分布式动态图的训练，使用 pytorch 和 paddle 完成在曙光超算上的分布式训练 + 对文档中代码进行的重写，导入了 paddle.vision 的部分包 + 对鲜花数据集加载的代码进行了修改，改为： +```python + train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) +``` +```python + optimizer.minimize(avg_loss)改为 optimizer.step() +``` + 鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。 + +- 4、文档质量评估，对 paddle 文档质量进行评估 + 文档中的代码有些旧，比如分布式训练快速开始中 1.3 动态图训练的代码许多 API 比较旧。 + 鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。在文档质量评估方面，认为文档对错误报告的解决方案不足。 + +- 5、错误汇总 + + +# 二、环境配置 + +## （1）在曙光超算昆山计算服务器部署 pytorch 分布式环境，给出部署步骤 +- 首先安装 anaconda +```python +bash Anaconda3-2020.07-Linux-x86_64.sh –u +``` +- 创建并进入 python3.6 环境 +```python +conda create -n pytorch-1.9 python=3.6 +conda activate pytorch-1.9 +``` +- 安装 pytorch-1.9（适配 rocm-4.0.1 及以上）PyTorch1.8 和 PyTorch1.9 安装 wheel 包在公共目录： +```python +/public/software/apps/DeepLearning/whl/rocm-4.0.1/ +``` +- 安装 pytorch_1.9-rocm_4.0.1(使用清华源) +```python +pip install /public/software/apps/DeepLearning/whl/rocm-4.0.1/torch-1.9.0+rocm4.0.1-cp36-cp36m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ +``` +- 对于 torchverion 的安装不能按照曙光官方给定的方法来，否则在 torchversion 在运行自定义算子时会出现错误，所以需要进行源码安装，安装方法如下: +```python +1、本地下载对应的 torchvision 分支源码包：https://github.com/pytorch/vision 上传集群， +2、进入对应的 conda 环境，加载对应的 rocm（这里 rocm4.0.1）版本； +3、conda install libpng -y +4、conda install jpeg -y +5、pip3 install numpy pillow matplotlib ninja -i https://pypi.tuna.tsinghua.edu.cn/simple/ +6、使用 salloc 申请计算结点，使用 ssh 登录至计算节点，并进入对应的 conda 环境加载 rocm（这里 rocm4.0.1），执行编译：CC=clang CXX=clang++ python setup.py install +``` +## （2）在曙光超算昆山计算服务器部署 paddle 分布式环境，给出部署步骤 +若在曙光平台不能使用 sudo 指令，从而导致不能直接使用 docker pull 方式安装镜像，可以直接使用曙光内置的可视化容器方式安装： +- 1、点击我的服务，计算智能服务 +- 2、点击容器服务 +- 3、点击容器管理添加镜像 +- 4、镜像添加，源镜像名称填：paddlepaddle/paddle，源镜像标签填：latest-dev-rocm4.0-miopen2.11 +- 5、添加完成后添加完成即可添加 +- 6、使用镜像，对镜像进行推送，快速访问选择，是 +- 7、点击 AI 服务，点击之前创建的容器 +- 8、点击启动容器，即可启动 rocm-4.0.1 环境 +- 9、打开容器后，该计算环境不能连接互联网，该环境可以调用此前终端设置的一切文件以及环境（包括 conda 环境），故需要提取打开命令行（E-Shell）创建一个 conda 环境安装 paddle。 +- 10、曙光服务器 conda 环境安装方式参考曙光超算官方链接： +```python +https://www.hpccube.com/doc/1.0.6/11250/general-handbook/compile/Anaconda.html +``` +- 11、激活 conda 环境并新建一个 conda 环境，进入该环境 +```python +source activate +conda create -n paddle python=3.7 +conda activate paddle +``` +- 12、在曙光上使用 paddle 官网给出的安装方式会出现错误。 +```python +python -m pip install paddlepaddle-rocm==2.2.2.rocm401.miopen211 -f https://www.paddlepaddle.org.cn/whl/rocm/stable.whl（此方法在曙光无法安装） +``` +- 13、故需要提前下载 whl 文件，下载链接： +```python +https://www.paddlepaddle.org.cn/whl/rocm/stable.whl +``` +- 14、paddlepaddle_rocm-2.2.2-cp37-cp37m-linux_x86_64.whl，版本经过测试可以安装。安装指令: +```python +pip install paddlepaddle_rocm-2.2.2-cp37-cp37m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ +``` +- 15、在安装完上述操作后还需要手动安装两个库 opencv-python 以及 scipy +```python +pip install scipy -i https://pypi.tuna.tsinghua.edu.cn/simple/ +pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple/ +``` + +## （3）对比两者的易用性与区别 +PyTorch 的分布式环境在曙光平台安装时需要手动编译 torchversion，这一点上 pytorch 比较繁琐。但是 pytorch 的环境在曙光平台比较稳定，而 paddle 环境在曙光平台经常不稳定，有时候能运行，有时候不能运行。 +![image](https://user-images.githubusercontent.com/102226413/164142960-e956efce-a8fe-40ea-bfba-a83b8f8203c5.png) + +上述问题是 rocm 版本问题，需要使用 rocm-4.0.1 版本。 修改 rocm 版本的方法为. module switch compiler/rocm/4.0.1 + +另外有一些问题没办法解决，我们使用的办法是重新开启镜像（多次开启后就会有可以使用的时候）无法解决的问题截图如下： +![image](https://user-images.githubusercontent.com/102226413/164143125-70d0e4ff-46d7-4461-8cb0-72c14e98b8e0.png) + +![image](https://user-images.githubusercontent.com/102226413/164143166-cde2793b-eb06-43a3-92d1-bfa68c2f1558.png) + + +另外，我们在曙光上使用 paddle 的方法为开启镜像的方式，但是曙光平台对 docker 镜像的支持不太好，每次镜像保持的时间最多为 72 小时，而且每次关闭镜像后，无法重新开启原先镜像。为了方便使用，希望能够支持 任务提交方式运行的 paddle 分布式框架。而且任务提交的方式还方便管理多节点运行。 + + +# 三、Fleet API 的使用 +## （1）分析 pytorch 分布式框架 DDP 某些 API 的使用 +- 导入必要的分布式训练依赖包 +```python +import torch.distributed as dist +``` +- 初始化 DDP 分布式环境，需要指定 backend, init_method,world_size, rank 四个参数 +```python +dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) +``` +- 优化器，不需要进行分布式函数的包装 +- 通过 DistributedDataParallel 获取分布式 model，用于支持分布式训练 +```python +model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank]) +``` + +## （2）按照文档内容使用 Fleet API +文档链接如下：https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/cluster_quick_start_cn.html +本次实验测试的 Fleet API 如下： +- 导入必要的分布式训练依赖包 +```python +from paddle.distributed import fleet +``` +- 初始化 Fleet 环境 +```python +fleet.init(is_collective=True) +``` +- 分布式优化器 +```python +optimizer = fleet.distributed_optimizer(optimizer) +``` +- 通过 Fleet API 获取分布式 model，用于支持分布式训练 +```python +resnet = fleet.distributed_model(resnet) +``` +## （3）比较 +pytorch 和 paddle 的分布式代码基本相似，pytorch 初始化 DDP 分布式环境，需要指定 backend, init_method,world_size, rank 四个参数，比较麻烦。 + +# 四、分布式动态图训练 + +## （1）使用 pytorch 完成一个图像分类的动态图分布式例子 + +DDP 分布式代码，测试 flower 数据集： +```python +import torch.nn as nn +import torch.utils.data as D +from torchvision import transforms +import torchvision +import torch +import os +import scipy.io as scio +import tarfile +import numpy as np +from PIL import Image +import torch.distributed as dist +MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': 'valid'} + + +class Flowers(torch.utils.data.Dataset): + def __init__(self, + data_file=None, + label_file=None, + setid_file=None, + mode='train', + transform=None, + backend=None): + assert mode.lower() in ['train', 'valid', 'test'], \ + "mode should be 'train', 'valid' or 'test', but got {}".format(mode) + + if backend is None: + backend = torchvision.get_image_backend() + self.backend = backend + + flag = MODE_FLAG_MAP[mode.lower()] + self.transform = transform + data_tar = tarfile.open(data_file) + self.data_path = data_file.replace(".tgz", "/") + if not os.path.exists(self.data_path): + os.mkdir(self.data_path) + data_tar.extractall(self.data_path) + + self.labels = scio.loadmat(label_file)['labels'][0] + self.indexes = scio.loadmat(setid_file)[flag][0] + + def __getitem__(self, idx): + index = self.indexes[idx] + label = np.array([self.labels[index - 1]]) + img_name = "jpg/image_%05d.jpg" % index + image = os.path.join(self.data_path, img_name) + image = Image.open(image) + if self.transform is not None: + image = self.transform(image) + + return image, label.astype('int64') + + def __len__(self): + return len(self.indexes) + + +def is_dist_avail_and_initialized(): + """检查是否支持分布式环境""" + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def reduce_value(value, average=True): + world_size = get_world_size() + if world_size < 2: # 单 GPU 的情况 + return value + + with torch.no_grad(): + dist.all_reduce(value) + if average: + value /= world_size + + return value + +def main(): + # 初始化 + rank = int(os.environ["RANK"]) + world_size = int(os.environ['WORLD_SIZE']) + gpu = int(os.environ['LOCAL_RANK']) + device = gpu + torch.cuda.set_device(gpu) + print("rank:", rank, "world size:", world_size, "gpu:", gpu) + dist.init_process_group(backend="nccl", world_size=world_size, rank=rank) + # 数据增强 + data_transforms = { + 'train': transforms.Compose([ + transforms.RandomRotation(45), # 随机旋转，-45 到 45 度之间随机 + transforms.CenterCrop(224), # 从中心开始裁剪 + transforms.RandomHorizontalFlip(p=0.5), # 随机水平翻转 选择一个概率 + transforms.RandomVerticalFlip(p=0.5), # 随机垂直翻转 + transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1), # 参数 1 为亮度，参数 2 为对比度，参数 3 为饱和度，参数 4 为色相 + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], + [0.229, 0.224, 0.225]) # 均值，标准差 + ]), + 'valid': transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], + [0.229, 0.224, 0.225]) + ]), + 'test': transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], + [0.229, 0.224, 0.225]) + ]) + } + batch_size = 32 + # 数据集构建与切分 + train_dataset = Flowers(mode='train', data_file="./102flowers.tgz", label_file="./imagelabels.mat", setid_file="./setid.mat", transform=data_transforms['train']) + train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) + train_loader = torch.utils.data.DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size) + # 模型构建 + model = torchvision.models.resnet50(pretrained=False, num_classes=102).to(device) + # 构建 DDP 模型 + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu]) + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.0001, momentum=0.9) + # 开始训练 + epochs = 100 + best_acc = 0.0 + for epoch in range(epochs): + if int(rank) == 0: + print('Epoch {}/{}'.format(epoch + 1, epochs)) + running_loss = 0.0 + running_corrects = 0 + train_sampler.set_epoch(epoch) + model.train() + for inputs, labels in train_loader: + inputs = inputs.to(device) + labels = labels.squeeze() - 1 + labels = labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + if int(rank) == 0: + print("loss:", loss) + loss.backward() + optimizer.step() + _, preds = torch.max(outputs, 1) + running_loss += loss.item() + running_corrects += torch.sum(preds == labels) + running_corrects = reduce_value(running_corrects, average=False) + epoch_loss = running_loss / len(train_loader) + epoch_acc = 1.0 * running_corrects.item() / train_sampler.total_size + best_acc = max(best_acc, epoch_acc) + if int(rank) == 0: + print('{} Loss: {:.4f} Acc: {:.4f}'.format( + 'train', epoch_loss, epoch_acc)) + + if int(rank) == 0: + print('Training complete Best val Acc: {:4f}'.format(best_acc)) + +if __name__ == "__main__": + main() + +``` + +```python +python -m torch.distributed.launch --nproc_per_node=4 train.py +``` + +## （2）使用 paddle 完成一个图像分类的分布式例子 +由于文档中提供的代码出现了较多问题，我们对代码进行了重新的编写。修改的部分有： +- 1、导入了 paddle.vision 的部分包， +- 2、对鲜花数据集加载的代码进行了修改，改为： +- train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) + +官网的代码不能再曙光平台下载鲜花数据集，所以我们需要提取下载离线鲜花数据集报错如下： +![image](https://user-images.githubusercontent.com/102226413/164143513-24236f90-975d-47f1-a1db-7e18e6c94c9c.png) + +且数据集的保存地址为一个缓存空间，用户在使用的时候可能找不到数据集，如/public/home/username/.cache/paddle/dataset 目录。 +而 pytorch 的加载数据集 API 会吧数据集加载到当前目录，方便了使用者。 +![image](https://user-images.githubusercontent.com/102226413/164144065-2fea8ac3-dcf5-48ac-a4c7-05cace99c611.png) + + + +- 3、optimizer.minimize(avg_loss)改为 optimizer.step() +- 4、鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。 + +train_fleet_dygraph.py 重构后的代码： +```python +# -*- coding: UTF-8 -*- +import numpy as np +import paddle +# 导入必要分布式训练的依赖包 +from paddle.distributed import fleet +# 导入模型文件 +from paddle.vision.models import ResNet +from paddle.vision.models.resnet import BottleneckBlock +from paddle.io import Dataset, BatchSampler, DataLoader, DistributedBatchSampler +from paddle.vision.transforms import Compose +from paddle.vision.transforms import ToTensor +from paddle.vision.transforms import Resize +base_lr = 0.1 # 学习率 +momentum_rate = 0.9 # 冲量 +l2_decay = 1e-4 # 权重衰减 + +epoch = 10 # 训练迭代次数 +batch_size = 32 # 训练批次大小 +class_dim = 102 + + +# 设置数据读取器 +# def reader_decorator(reader): +# def __reader__(): +# for item in reader(): +# img = np.array(item[0]).astype('float32').reshape(3, 224, 224) +# label = np.array(item[1]).astype('int64').reshape(1) +# yield img, label +# +# return __reader__ + + +# 设置优化器 +def optimizer_setting(parameter_list=None): + optimizer = paddle.optimizer.Momentum( + learning_rate=base_lr, + momentum=momentum_rate, + weight_decay=paddle.regularizer.L2Decay(l2_decay), + parameters=parameter_list) + return optimizer + + +# 设置训练函数 +def train_resnet(): + # 初始化 Fleet 环境 + fleet.init(is_collective=True) + + # resnet = resnet34(class_dim=class_dim, layers=50) + resnet = ResNet(BottleneckBlock, 50, num_classes=class_dim) + + optimizer = optimizer_setting(parameter_list=resnet.parameters()) + # 分布式优化器 + optimizer = fleet.distributed_optimizer(optimizer) + # 通过 Fleet API 获取分布式 model，用于支持分布式训练 + resnet = fleet.distributed_model(resnet) + # 构建分布式数据集 归一化 / 255 并且转成 HWC --> CHW 格式 + # transform = ToTensor() + transform = Compose([ + Resize(size=(224, 224)), + ToTensor() + ]) + train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) + # 数据集的拆分 构建分布式数据集 + train_sampler = DistributedBatchSampler(train_dataset, 16, shuffle=True) + # , num_workers=2 + train_loader = DataLoader(train_dataset, batch_sampler=train_sampler) + + # train_reader = paddle.batch( + # reader_decorator(paddle.dataset.flowers.train(use_xmap=True)), + # # reader_decorator(paddle.vision.datasets.Cifar10(mode='train', backend='cv2')), + # batch_size=batch_size, + # drop_last=True) + # + # train_loader = paddle.io.DataLoader.from_generator( + # capacity=32, + # use_double_buffer=True, + # iterable=True, + # return_list=True, + # use_multiprocess=True) + + # train_loader.set_sample_list_generator(train_reader) + + for eop in range(epoch): + resnet.train() + for batch_id, data in enumerate(train_loader()): + img, label = data + label = label - 1 + label.stop_gradient = True + # 前向传播 + out = resnet(img) + loss = paddle.nn.functional.cross_entropy(input=out, label=label) + avg_loss = paddle.mean(x=loss) + acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) + acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) + dy_out = avg_loss.numpy() + avg_loss.backward() + # + optimizer.step() + resnet.clear_gradients() + if batch_id % 5 == 0: + print("[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f" % ( + eop, batch_id, dy_out, acc_top1, acc_top5)) + + +# 启动训练 +if __name__ == '__main__': + train_resnet() +``` + +运行方式： +```python +python3 -m paddle.distributed.launch --gpus=0,1,2,3 train_fleet_dygraph.py +``` + +## （3）两个程序的运行结果 + +- DDP 程序运行结果 +```python +Epoch 7/100 +loss: tensor(4.3571, device='cuda:0', grad_fn=) +loss: tensor(4.1223, device='cuda:0', grad_fn=) +loss: tensor(4.3427, device='cuda:0', grad_fn=) +loss: tensor(4.4527, device='cuda:0', grad_fn=) +loss: tensor(4.4157, device='cuda:0', grad_fn=) +loss: tensor(4.4089, device='cuda:0', grad_fn=) +loss: tensor(4.4046, device='cuda:0', grad_fn=) +loss: tensor(4.4396, device='cuda:0', grad_fn=) +loss: tensor(4.3414, device='cuda:0', grad_fn=) +loss: tensor(4.3834, device='cuda:0', grad_fn=) +loss: tensor(4.2856, device='cuda:0', grad_fn=) +loss: tensor(4.4135, device='cuda:0', grad_fn=) +loss: tensor(4.4047, device='cuda:0', grad_fn=) +loss: tensor(4.4164, device='cuda:0', grad_fn=) +loss: tensor(4.3926, device='cuda:0', grad_fn=) +loss: tensor(4.2654, device='cuda:0', grad_fn=) +loss: tensor(4.2372, device='cuda:0', grad_fn=) +loss: tensor(4.4528, device='cuda:0', grad_fn=) +loss: tensor(4.2135, device='cuda:0', grad_fn=) +loss: tensor(4.2939, device='cuda:0', grad_fn=) +loss: tensor(4.4517, device='cuda:0', grad_fn=) +loss: tensor(4.1285, device='cuda:0', grad_fn=) +loss: tensor(4.4465, device='cuda:0', grad_fn=) +loss: tensor(4.5095, device='cuda:0', grad_fn=) +loss: tensor(4.3783, device='cuda:0', grad_fn=) +loss: tensor(4.4235, device='cuda:0', grad_fn=) +loss: tensor(4.5945, device='cuda:0', grad_fn=) +loss: tensor(4.4677, device='cuda:0', grad_fn=) +loss: tensor(4.2633, device='cuda:0', grad_fn=) +loss: tensor(4.2308, device='cuda:0', grad_fn=) +loss: tensor(4.4648, device='cuda:0', grad_fn=) +loss: tensor(4.3647, device='cuda:0', grad_fn=) +loss: tensor(4.2926, device='cuda:0', grad_fn=) +loss: tensor(4.3164, device='cuda:0', grad_fn=) +loss: tensor(4.3357, device='cuda:0', grad_fn=) +loss: tensor(4.5612, device='cuda:0', grad_fn=) +loss: tensor(4.4285, device='cuda:0', grad_fn=) +loss: tensor(4.6838, device='cuda:0', grad_fn=) +loss: tensor(4.3026, device='cuda:0', grad_fn=) +loss: tensor(4.3654, device='cuda:0', grad_fn=) +loss: tensor(4.5110, device='cuda:0', grad_fn=) +loss: tensor(4.2723, device='cuda:0', grad_fn=) +loss: tensor(4.4866, device='cuda:0', grad_fn=) +loss: tensor(4.6240, device='cuda:0', grad_fn=) +loss: tensor(4.4451, device='cuda:0', grad_fn=) +loss: tensor(4.4438, device='cuda:0', grad_fn=) +loss: tensor(4.3717, device='cuda:0', grad_fn=) +loss: tensor(4.2463, device='cuda:0', grad_fn=) +loss: tensor(4.2591, device='cuda:0', grad_fn=) +train Loss: 4.3819 Acc: 0.0439 +Epoch 8/100 +loss: tensor(4.4381, device='cuda:0', grad_fn=) +loss: tensor(4.4635, device='cuda:0', grad_fn=) +loss: tensor(4.3493, device='cuda:0', grad_fn=) +loss: tensor(4.2414, device='cuda:0', grad_fn=) +loss: tensor(4.2735, device='cuda:0', grad_fn=) +loss: tensor(4.5272, device='cuda:0', grad_fn=) +loss: tensor(4.1665, device='cuda:0', grad_fn=) +loss: tensor(4.4553, device='cuda:0', grad_fn=) +loss: tensor(4.3675, device='cuda:0', grad_fn=) +loss: tensor(4.5145, device='cuda:0', grad_fn=) +loss: tensor(4.3524, device='cuda:0', grad_fn=) +loss: tensor(4.4013, device='cuda:0', grad_fn=) +loss: tensor(4.4338, device='cuda:0', grad_fn=) +loss: tensor(4.2520, device='cuda:0', grad_fn=) +loss: tensor(4.1550, device='cuda:0', grad_fn=) +loss: tensor(4.2074, device='cuda:0', grad_fn=) +loss: tensor(4.2859, device='cuda:0', grad_fn=) +loss: tensor(4.5531, device='cuda:0', grad_fn=) +loss: tensor(4.6937, device='cuda:0', grad_fn=) +loss: tensor(4.3387, device='cuda:0', grad_fn=) +loss: tensor(4.4027, device='cuda:0', grad_fn=) +loss: tensor(4.3194, device='cuda:0', grad_fn=) +loss: tensor(4.5824, device='cuda:0', grad_fn=) +loss: tensor(4.3335, device='cuda:0', grad_fn=) +loss: tensor(4.4477, device='cuda:0', grad_fn=) +loss: tensor(4.2184, device='cuda:0', grad_fn=) +loss: tensor(4.2377, device='cuda:0', grad_fn=) +loss: tensor(4.4388, device='cuda:0', grad_fn=) +loss: tensor(4.2814, device='cuda:0', grad_fn=) +loss: tensor(4.4338, device='cuda:0', grad_fn=) +loss: tensor(4.4741, device='cuda:0', grad_fn=) +loss: tensor(4.3977, device='cuda:0', grad_fn=) +loss: tensor(4.5670, device='cuda:0', grad_fn=) +loss: tensor(4.2727, device='cuda:0', grad_fn=) +loss: tensor(4.5136, device='cuda:0', grad_fn=) +loss: tensor(4.2349, device='cuda:0', grad_fn=) +loss: tensor(4.2736, device='cuda:0', grad_fn=) +loss: tensor(4.4575, device='cuda:0', grad_fn=) +loss: tensor(4.2906, device='cuda:0', grad_fn=) +loss: tensor(4.2580, device='cuda:0', grad_fn=) +loss: tensor(4.5590, device='cuda:0', grad_fn=) +loss: tensor(4.2204, device='cuda:0', grad_fn=) +loss: tensor(4.5499, device='cuda:0', grad_fn=) +loss: tensor(4.3762, device='cuda:0', grad_fn=) +loss: tensor(4.4189, device='cuda:0', grad_fn=) +loss: tensor(4.3005, device='cuda:0', grad_fn=) +loss: tensor(4.4099, device='cuda:0', grad_fn=) +loss: tensor(4.2168, device='cuda:0', grad_fn=) +loss: tensor(4.1337, device='cuda:0', grad_fn=) +train Loss: 4.3692 Acc: 0.0447 +``` + + +- paddlepaddle 运行结果，附了第 7、8 个 epoch +```python +launch train in GPU mode! +INFO 2022-04-02 19:30:53,752 launch_utils.py:510] Local start 4 processes. First process distributed environment info (Only For Debug): + +=======================================================================================+ + | Distributed Envs Value | + +---------------------------------------------------------------------------------------+ + | PADDLE_TRAINER_ID 0 | + | PADDLE_CURRENT_ENDPOINT 127.0.0.1:60409 | + | PADDLE_TRAINERS_NUM 4 | + | PADDLE_TRAINER_ENDPOINTS ... 0.1:41442,127.0.0.1:51971,127.0.0.1:52550| + | PADDLE_RANK_IN_NODE 0 | + | PADDLE_LOCAL_DEVICE_IDS 0 | + | PADDLE_WORLD_DEVICE_IDS 0,1,2,3 | + | FLAGS_selected_gpus 0 | + | FLAGS_selected_accelerators 0 | + +=======================================================================================+ + +INFO 2022-04-02 19:30:53,752 launch_utils.py:514] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 +launch proc_id:12642 idx:0 +launch proc_id:12645 idx:1 +launch proc_id:12648 idx:2 +launch proc_id:12651 idx:3 + +Epoch 7, batch 0] loss: 4.30898, acc1: 0.00000, acc5: 0.12500 +[Epoch 7, batch 5] loss: 4.07224, acc1: 0.06250, acc5: 0.18750 +[Epoch 7, batch 10] loss: 4.15420, acc1: 0.12500, acc5: 0.25000 +[Epoch 7, batch 15] loss: 4.47023, acc1: 0.06250, acc5: 0.31250 +[Epoch 7, batch 20] loss: 4.43729, acc1: 0.00000, acc5: 0.06250 +[Epoch 7, batch 25] loss: 4.22468, acc1: 0.00000, acc5: 0.12500 +[Epoch 7, batch 30] loss: 3.64519, acc1: 0.18750, acc5: 0.50000 +[Epoch 7, batch 35] loss: 4.45402, acc1: 0.06250, acc5: 0.12500 +[Epoch 7, batch 40] loss: 4.19298, acc1: 0.06250, acc5: 0.12500 +[Epoch 7, batch 45] loss: 3.99954, acc1: 0.00000, acc5: 0.37500 +[Epoch 7, batch 50] loss: 4.40063, acc1: 0.06250, acc5: 0.25000 +[Epoch 7, batch 55] loss: 4.24690, acc1: 0.00000, acc5: 0.25000 +[Epoch 7, batch 60] loss: 4.49993, acc1: 0.00000, acc5: 0.25000 +[Epoch 7, batch 65] loss: 4.41674, acc1: 0.00000, acc5: 0.06250 +[Epoch 7, batch 70] loss: 4.08913, acc1: 0.00000, acc5: 0.31250 +[Epoch 7, batch 75] loss: 4.19635, acc1: 0.00000, acc5: 0.12500 +[Epoch 7, batch 80] loss: 3.75817, acc1: 0.12500, acc5: 0.43750 +[Epoch 7, batch 85] loss: 4.29419, acc1: 0.06250, acc5: 0.25000 +[Epoch 7, batch 90] loss: 3.99528, acc1: 0.00000, acc5: 0.12500 +[Epoch 7, batch 95] loss: 4.32901, acc1: 0.06250, acc5: 0.37500 +[Epoch 8, batch 0] loss: 4.41281, acc1: 0.00000, acc5: 0.12500 +[Epoch 8, batch 5] loss: 4.10598, acc1: 0.12500, acc5: 0.25000 +[Epoch 8, batch 10] loss: 4.27404, acc1: 0.00000, acc5: 0.18750 +[Epoch 8, batch 15] loss: 3.97948, acc1: 0.06250, acc5: 0.06250 +[Epoch 8, batch 20] loss: 4.49495, acc1: 0.00000, acc5: 0.06250 +[Epoch 8, batch 25] loss: 4.07579, acc1: 0.00000, acc5: 0.25000 +[Epoch 8, batch 30] loss: 3.63573, acc1: 0.12500, acc5: 0.37500 +[Epoch 8, batch 35] loss: 3.79878, acc1: 0.18750, acc5: 0.31250 +[Epoch 8, batch 40] loss: 4.38518, acc1: 0.06250, acc5: 0.25000 +[Epoch 8, batch 45] loss: 4.08105, acc1: 0.12500, acc5: 0.25000 +[Epoch 8, batch 50] loss: 4.33881, acc1: 0.12500, acc5: 0.18750 +``` + +# 五、文档质量 +感觉文档中部分代码的版本较老，比如 1.3 动态图完整代码中： +```python +from resnet_dygraph import ResNet +``` +上述操作可以使用 paddle 内置 API 进行替换 +```python +from paddle.vision.models import ResNet +``` +flower 数据集在曙光平台不能通过 API 直接加载得到，需要手动下载。加载鲜花数据集的 API 也需要进行更新。可以更新为 +```python +train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) +``` +对于很多错误，在文档中没有一个很好的提示。可以对常见报错进行一个汇总。 + + +# 六、报错查错（问题汇总） +1、paddle 在曙光超算上运行出现 libamdhip64.4,，需要指定使用 rocm4.0.1 环境运行，曙光上 rocm-2.9 环境不能运行。 +```python +module rm compiler/rocm/2.9 +module load compiler/rocm/4.0.1 +``` + +![image](https://user-images.githubusercontent.com/102226413/164144235-ce808c51-1712-4417-b9bf-99da6362b3f0.png) + + +2、无法下载 flower 数据集，需要手动加载数据集 +按照文档旧 API 无法在曙光平台以及移动九天平台加载数据集，需要手动下载数据集。 +且数据集的保存地址为一个缓存空间，用户在使用的时候可能找不到数据集，如/public/home/username/.cache/paddle/dataset 目录。 +而 pytorch 的加载数据集 API 会吧数据集加载到当前目录，方便了使用者。 + +![image](https://user-images.githubusercontent.com/102226413/164143513-24236f90-975d-47f1-a1db-7e18e6c94c9c.png) + +![image](https://user-images.githubusercontent.com/102226413/164144065-2fea8ac3-dcf5-48ac-a4c7-05cace99c611.png) + +3、安装完 paddle 后运行该程序会缺少常用两个库：opencv-python 以及 scipy。 +安装方式： +```python +pip install scipy -i https://pypi.tuna.tsinghua.edu.cn/simple/ +pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple/ +``` +4、鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。 +```python +img, label = data +label = label - 1 +``` + +5、未解决问题（无法在曙光上使用 paddle 的问题） +![image](https://user-images.githubusercontent.com/102226413/164143125-70d0e4ff-46d7-4461-8cb0-72c14e98b8e0.png) + +![image](https://user-images.githubusercontent.com/102226413/164143166-cde2793b-eb06-43a3-92d1-bfa68c2f1558.png) diff --git "a/docs/eval/\343\200\220Hackathon No.113\343\200\221 PR.md" "b/docs/eval/\343\200\220Hackathon No.113\343\200\221 PR.md" new file mode 100644 index 00000000000..b6bdcde327f --- /dev/null +++ "b/docs/eval/\343\200\220Hackathon No.113\343\200\221 PR.md" @@ -0,0 +1,1139 @@ + +# 1、任务描述： + + 飞桨框架于 2.0 正式版全面支持了动态图训练，并在 2.1、2.2 两个大版本中不断完善分布式能力，同时大幅增强了训练功能。在本任务中，我们希望能收到你对于飞桨动态图分布式训练功能的使用感受，可以与其他深度学习框架做功能对比，并产出一份对应的评估报告。 + +# 2、环境配置： + + 因为需要将飞桨 PaddlePaddle 框架的分布式与其他深度学习框架做功能对比，这里其他深度学习框架我选择了 PyTorch 框架，所以首先需要安装飞桨 PaddlePaddle 框架与 PyTorch 框架，开发平台选择了曙光平台昆山超算。 + +## 2.1、PyTorch 环境配置： + +- 1、首先安装 anaconda +```bash +bash Anaconda3-2020.07-Linux-x86_64.sh –u +``` +- 2、创建一个属于自己的环境并激活 +```bash +conda create --name pytorch_1.9 python=3.7 +conda activate pytorch-1.9 +``` +- 3、安装 pytorch-1.9（适配 rocm-4.0.1 及以上）PyTorch1.8 和 PyTorch1.9 安装 wheel 包在公共目录： +```bash +/public/software/apps/DeepLearning/whl/rocm-4.0.1/ +``` +- 安装指令如下： +```bash +module rm compiler/rocm/2.9 +module load compiler/rocm/4.0.1 +pip install /public/software/apps/DeepLearning/whl/rocm-4.0.1/torch-1.9.0+rocm4.0.1-cp36-cp36m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ +``` +- 对于 torchverion 的安装不能按照曙光官方帮助文档给定的方法来，否则 torchvision 在运行自定义算子时会出现错误，所以需要使用源码安装的方式，安装方法如下: +```text +1、本地下载对应的 torchvision 分支源码包：https://github.com/pytorch/vision 上传集群， +2、进入对应的 conda 环境，加载对应的 rocm（这里 rocm4.0.1）版本； +3、conda install libpng -y +4、conda install jpeg -y +5、pip3 install numpy pillow matplotlib ninja -i https://pypi.tuna.tsinghua.edu.cn/simple/ +6、使用 salloc 申请计算结点，使用 ssh 登录至计算节点，并进入对应的 conda 环境加载 rocm（这里 rocm4.0.1），执行编译：CC=clang CXX=clang++ python setup.py install +``` + +## 2.2、PaddlePaddle 环境配置： + +- PaddlePaddle 的环境在曙光超算上需要使用镜像的方式进行安装，镜像添加，源镜像名称填：paddlepaddle/paddle，源镜像标签填：latest-dev-rocm4.0-miopen2.11。然后创建实例打开容器。因为 Docker 容器中不能连接网络，使用 paddle 官网给出的安装方式会出现网络连接的错误。 +```bash +python -m pip install paddlepaddle-rocm==2.2.2.rocm401.miopen211 -f https://www.paddlepaddle.org.cn/whl/rocm/stable.whl +``` +- 故需要提前下载 whl 文件，下载链接如下，下载的版本为 paddlepaddle_rocm-2.1.1.rocm401.miopen211-cp37-cp37m-linux_x86_64.whl。下载链接： + +https://www.paddlepaddle.org.cn/whl/rocm/stable.whl + +- 安装指令为 +```bash +pip install paddlepaddle_rocm-2.1.1.rocm401.miopen211-cp37-cp37m-linux_x86_64.whl +``` +- 期间所需要的其他库都需要通过在曙光超算上通过 EShell 进行安装，需要设定清华镜像源，例如 +```bash +pip install six -i https://pypi.tuna.tsinghua.edu.cn/simple +``` +- 经过测试，测试指令为 +```bash +python -c "import paddle; paddle.utils.run_check()" +``` +环境有效。 + +## 2.3、安装对比分析 + +PyTorch 的分布式环境在曙光平台安装时需要手动源码编译 torchversion，这个过程比较慢，这一点上 PyTorch 相对比较繁琐；但是 PyTorch 的环境在曙光平台比较稳定，而 PaddlePaddle 环境在曙光平台不太稳定。 + +# 3、Paddle 单机与分布式： + +## 3.1、Paddle 单机 + +在图像处理中，关键点本质上是一种特征。它是对一个固定区域或者空间物理关系的抽象描述，描述的是一定邻域范围内的组合或上下文关系。它不仅仅是一个点信息，或代表一个位置，更代表着上下文与周围邻域的组合关系。关键点检测的目标就是通过计算机从图像中找出这些点的坐标，作为计算机视觉领域的一个基础任务，关键点的检测对于高级别任务，例如识别和分类具有至关重要的意义。任务选用的是人脸关键点检测，采用的方法是坐标点回归的方式进行，参考 PaddlePaddle 网址如下： +https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/landmark_detection.html + +其思路如下： +- 1、导入相关库 +- 2、构建数据集 +- 3、定义模型 +- 4、构建损失函数与优化器 +- 5、训练模型 +- 6、预测 + +具体分析如下： +- 第一步：导入相关库，即导入与本次任务相关的库，例如 import paddle +- 第二步：构建数据集，首先自定义一个处理人脸的 datasets，其继承于 Dataset 类，然后实现__init__,__getitem__与__len__函数，__init__函数实现一些数据集的初始化，即完成了对 csv 文件的读取与数据的清洗以及训练集、验证集、测试集的划分，__getitem__函数实现了通过 index 参数获取对应的 image 与 label，同时对图像进行预处理操作，即 transform 操作，__len__函数即返回数据集的数量（长度）。 +- 第三步：定义模型，构建一个模型的类，继承于 paddle.nn.Layer，然后实现__init__与 forward 函数，__init__函数定义网络模型的一些层结构，forward 函数实现网络模型的前向传播。同时，示例中给出的代码中网络模型经过了 paddle.Model 类的封装，paddle.Model 类相对来说会更方便一些，其内部定义了一些 fit 等函数可以直接使用。 +- 第四步：定义损失函数与优化器，优化器采用了 Adam 算法，同时初始学习率指定为 0.001，损失函数采用均方误差函数，因为网络模型经过了 paddle.Model 类的封装，所以此处对于损失函数与优化器需要使用 prepare 函数进行指定。 +- 第五步：构建训练代码，网络模型经过了 paddle.Model 类的封装，训练只需要调用其内部的 fit 函数，对于 fit 函数，其内部会把传入的数据集进行 DataLoader 封装，同时通过 tain_batch 函数进行其训练过程。 +- 第六步：测试，通过调用 paddle.Model 类的 predict 函数可以直接得到其预测的结果。 +- 总结：上述单机的过程中，其采用了 paddle.Model 类的封装，会在使用方面方便很多，并且 paddle.Model 类的内部会在模型训练的过程中加入一些回调函数 callback 进行使用，相对来说比较好用一些。 + +## 3.2、paddle 分布式的实现 + +### 方式一： +首先对于上述代码进行分析，在 paddle.Model 的底层可以发现其在对模型进行初始化的时候会判断是动态图的模式还是静态图的模式，即 +```python +if fluid.in_dygraph_mode(): + self._adapter = DynamicGraphAdapter(self) +else: + self._adapter = StaticGraphAdapter(self) +``` +因为我们采用的是动态图，所以其会调用第一个函数，即初始化 DynamicGraphAdapter(self)，观察其底层内部我们可以发现其会进行单机还是分布式的判断，如果是分布式，即 tasks>1，会初始化分布式的环境同时构建分布式的模型，代码分析如下： + +```python +if self._nranks > 1: + dist.init_parallel_env() + stradegy = fluid.dygraph.parallel.ParallelStrategy() + stradegy.nranks = ParallelEnv().nranks + stradegy.local_rank = ParallelEnv().local_rank + stradegy.trainer_endpoints = ParallelEnv().trainer_endpoints + stradegy.current_endpoint = ParallelEnv().current_endpoint + self.ddp_model = fluid.dygraph.parallel.DataParallel( + self.model.network, stradegy) + +``` +这样的话，分布式的环境与分布式的模型就已经在 paddle.Model 内的内部构建完成了。 +分析 model.prepare 即设置训练过程中的优化器与损失函数中我们发现其内部并没有设置分布式优化器，故这一部分需要自己在代码中添加，即 +```python +optim = fleet.distributed_optimizer(optim) +model.prepare(optim, paddle.nn.MSELoss()) +``` +分析训练的过程，即 model.fit 的底层代码，我们可以发现其底层构建数据集是就采用了分布式的数据集划分，即其 sampler 采用的是 DistributedBatchSampler，所以其训练时也就采用的是分布式的训练过程，即把数据划分到各个设备上进行训练。 + +总结：相对来说，示例中的代码使用了 paddle.Model 的封装，而其 paddle.Model 底层的一些函数中也添加了对分布式的处理，所以从示例代码中改成分布式的代码只需要进行分布式优化器的构建，即将 +```python +model.prepare(optim, paddle.nn.MSELoss()) +``` +改成 +```python +optim = fleet.distributed_optimizer(optim) +model.prepare(optim, paddle.nn.MSELoss()) +``` +### 方式二： +paddle.Model 类内部封装的东西使用起来比较方便，但是不利于用户了解单机转成分布式的具体流程，所以我没有使用 paddle.Model 类，而是重新编写了分布式的代码。其流程如下： + +- 1、导入分布式所需要的依赖包 +- 2、初始化分布式环境 +- 3、构建模型 +- 4、设置分布式所需要的优化器 +- 5、数据集的拆分 +- 6、构建训练代码 +- 7、启动分布式任务 + +下面具体流程如下： +- 第一步：导入分布式所需要的依赖包，即导入任务相关的 API 函数，例如 +```python +from paddle.distributed import fleet +``` +- 第二步：初始化分布式环境，采用了 collective 通信，代码如下: +```python +fleet.init(is_collective=True) +``` +- 第三步：构建网络模型，这个网络模型采用了和示例单机中相同的网络模型。代码如下： +```python +model = FaceNet(num_keypoints=15) +model = fleet.distributed_model(model) +``` +- 第四步：设置分布式所需要的优化器，优化器采用了 Adam 优化器，初始学习率为 0.001，代码如下： +```python +optim = paddle.optimizer.Adam(learning_rate=1e-3, parameters=model.parameters()) + optim = fleet.distributed_optimizer(optim) +``` +- 第五步：数据集的拆分 +对于分布式的数据拆分，需要先构建其数据集的采样器，这里需要使用 DistributedBatchSampler，其中参数为数据集 dataset、batch_size、num_replicas、rank、shuffle、drop_last，这里我指定了 dataset、batch_size、shuffle，设置了 shuffle 为 True，即对数据进行打乱，其中的参数 num_replicas 如果不指定，其默认会获取当前环境中的 ntasks，然后按照 ntasks 分配数据集。drop_last 参数如果不指定会默认为 False，也就是不会丢失最后一个 batch 的数据。构建完分布式采样器之后，使用 DataLoader 进行封装一下，这里指定一下 batch_sampler 为刚才构建的采样器，注意指定 batch_sampler 参数之后不需要再指定 batch_size、shuffle 以及 drop_last 参数。 +- 第六步：构建训练代码 +这里不采用 paddle.Model 进行封装，所以需要自己编写 for 循环获取数据进行前向传播以及反向传播的过程，代码如下： +```python +for eop in range(epoch): + # train_sampler.set_epoch(eop) + # 设置为训练模式 + model.train() + for batch_id, data in enumerate(train_loader()): + img, label = data + label.stop_gradient = True + # 前向传播 + out = model(img) + # 均方损失函数 + loss = paddle.nn.functional.mse_loss(input=out, label=label) + loss_data = loss.numpy() + # 反向传播 + loss.backward() + optim.step() + model.clear_gradients() + if batch_id % 10 == 0: + print("[Epoch %d, batch %d] loss: %.5f" % (eop, batch_id, loss_data)) +``` +* 第七步： +飞桨通过 paddle.distributed.launch 组件启动分布式任务。该组件可用于启动单机多卡分布式任务，也可以用于启动多机多卡分布式任务。该组件为每张参与分布式任务的训练卡启动一个训练进程。默认情形下，该组件将在每个节点上启动 N 个进程，这里 N 等于训练节点的卡数，即使用所有的训练卡。用户也可以通过 gpus 参数指定训练节点上使用的训练卡列表，该列表以逗号分隔。需要注意的是，所有节点需要使用相同数量的训练卡数。为了启动多机分布式任务，需要通过 ips 参数指定所有节点的 IP 地址列表，该列表以逗号分隔。需要注意的是，该列表在所有节点上需要保持一致，即各节点 IP 地址出现的顺序需要保持一致。这里我进行了单机多卡与多机多卡的实验，实验启动方式如下： + +单机多卡分布式任务：这里我采用的是四个卡，启动方式如下： +```python +python -m paddle.distributed.launch --gpus 0,1,2,3 train_multi_gpu.py +``` +运行结果如下： +```python +----------- Configuration Arguments ----------- +gpus: 0,1,2,3 +heter_worker_num: None +heter_workers: +http_port: None +ips: 127.0.0.1 +log_dir: log +nproc_per_node: None +run_mode: None +server_num: None +servers: +training_script: train_multi_4.py +training_script_args: [] +worker_num: None +workers: +------------------------------------------------ +WARNING 2022-04-02 23:29:34,154 launch.py:359] Not found distinct arguments and compiled with cuda or xpu. Default use collective mode +launch train in GPU mode! +INFO 2022-04-02 23:29:34,156 launch_utils.py:510] Local start 4 processes. First process distributed environment info (Only For Debug): + +=======================================================================================+ + | Distributed Envs Value | + +---------------------------------------------------------------------------------------+ + | PADDLE_TRAINER_ID 0 | + | PADDLE_CURRENT_ENDPOINT 127.0.0.1:40344 | + | PADDLE_TRAINERS_NUM 4 | + | PADDLE_TRAINER_ENDPOINTS ... 0.1:41302,127.0.0.1:54290,127.0.0.1:60414| + | PADDLE_RANK_IN_NODE 0 | + | PADDLE_LOCAL_DEVICE_IDS 0 | + | PADDLE_WORLD_DEVICE_IDS 0,1,2,3 | + | FLAGS_selected_gpus 0 | + | FLAGS_selected_accelerators 0 | + +=======================================================================================+ + +INFO 2022-04-02 23:29:34,156 launch_utils.py:514] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 +launch proc_id:22388 idx:0 +launch proc_id:22391 idx:1 +launch proc_id:22394 idx:2 +launch proc_id:22397 idx:3 +W0402 23:29:36.404554 22388 gen_comm_id_helper.cc:120] connect addr=127.0.0.1:41302 failed 1 times with reason: Connection refused retry after 0.5 seconds +I0402 23:29:36.905133 22388 nccl_context.cc:74] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 0 +W0402 23:30:20.059859 22388 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 90.0, Driver API Version: 321.0, Runtime API Version: 3.1 +W0402 23:30:20.067070 22388 device_context.cc:417] device: 0, MIOpen Version: 2.11.0 +I0402 23:30:20.082139 22388 nccl_context.cc:107] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 10 + +/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. +Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations + if data.dtype == np.object: +2022-04-02 23:32:10,565-INFO: [topology.py:152:__init__] HybridParallelInfo: rank_id: 0, dp_degree: 4, mp_degree: 1, pp_degree: 1, dp_group: [0, 1, 2, 3], mp_group: [0], pp_group: [0], check/clip group: [0] +Epoch 1 / 30 +/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance. + "When training, we now always track global mean and variance.") +step 0 / 14 - loss: 0.91819 +step 10 / 14 - loss: 0.06710 +Eval begin... +step 0 / 4 - loss: 0.02265 +Eval samples: 428 +Epoch 2 / 30 +step 0 / 14 - loss: 0.04950 +step 10 / 14 - loss: 0.02599 +Eval begin... +step 0 / 4 - loss: 0.00559 +Eval samples: 428 +Epoch 3 / 30 +step 0 / 14 - loss: 0.02373 +step 10 / 14 - loss: 0.01906 +Eval begin... +step 0 / 4 - loss: 0.00270 +Eval samples: 428 +Epoch 4 / 30 +step 0 / 14 - loss: 0.01631 +step 10 / 14 - loss: 0.01318 +Eval begin... +step 0 / 4 - loss: 0.00227 +Eval samples: 428 +Epoch 5 / 30 +step 0 / 14 - loss: 0.01354 +step 10 / 14 - loss: 0.01125 +Eval begin... +step 0 / 4 - loss: 0.00136 +Eval samples: 428 +Epoch 6 / 30 +step 0 / 14 - loss: 0.01162 +step 10 / 14 - loss: 0.00890 +Eval begin... +step 0 / 4 - loss: 0.00145 +Eval samples: 428 +Epoch 7 / 30 +step 0 / 14 - loss: 0.01274 +step 10 / 14 - loss: 0.01234 +Eval begin... +step 0 / 4 - loss: 0.00259 +Eval samples: 428 +Epoch 8 / 30 +step 0 / 14 - loss: 0.00965 +step 10 / 14 - loss: 0.00664 +Eval begin... +step 0 / 4 - loss: 0.00200 +Eval samples: 428 +Epoch 9 / 30 +step 0 / 14 - loss: 0.00763 +step 10 / 14 - loss: 0.00707 +Eval begin... +step 0 / 4 - loss: 0.00129 +Eval samples: 428 +Epoch 10 / 30 +step 0 / 14 - loss: 0.00697 +step 10 / 14 - loss: 0.00591 +Eval begin... +step 0 / 4 - loss: 0.00150 +Eval samples: 428 +Epoch 11 / 30 +step 0 / 14 - loss: 0.00708 +step 10 / 14 - loss: 0.00488 +Eval begin... +step 0 / 4 - loss: 0.00376 +Eval samples: 428 +Epoch 12 / 30 +step 0 / 14 - loss: 0.00859 +step 10 / 14 - loss: 0.00602 +Eval begin... +step 0 / 4 - loss: 0.00262 +Eval samples: 428 +Epoch 13 / 30 +step 0 / 14 - loss: 0.00815 +step 10 / 14 - loss: 0.00786 +Eval begin... +step 0 / 4 - loss: 0.00187 +Eval samples: 428 +Epoch 14 / 30 +step 0 / 14 - loss: 0.00540 +step 10 / 14 - loss: 0.00839 +Eval begin... +step 0 / 4 - loss: 0.00235 +Eval samples: 428 +Epoch 15 / 30 +step 0 / 14 - loss: 0.00568 +step 10 / 14 - loss: 0.00725 +Eval begin... +step 0 / 4 - loss: 0.00189 +Eval samples: 428 +Epoch 16 / 30 +step 0 / 14 - loss: 0.00657 +step 10 / 14 - loss: 0.00842 +Eval begin... +step 0 / 4 - loss: 0.00112 +Eval samples: 428 +Epoch 17 / 30 +step 0 / 14 - loss: 0.00573 +step 10 / 14 - loss: 0.00642 +Eval begin... +step 0 / 4 - loss: 0.00297 +Eval samples: 428 +Epoch 18 / 30 +step 0 / 14 - loss: 0.00617 +step 10 / 14 - loss: 0.00632 +Eval begin... +step 0 / 4 - loss: 0.00399 +Eval samples: 428 +Epoch 19 / 30 +step 0 / 14 - loss: 0.00850 +step 10 / 14 - loss: 0.00664 +Eval begin... +step 0 / 4 - loss: 0.00083 +Eval samples: 428 +Epoch 20 / 30 +step 0 / 14 - loss: 0.00555 +step 10 / 14 - loss: 0.01062 +Eval begin... +step 0 / 4 - loss: 0.00103 +Eval samples: 428 +Epoch 21 / 30 +step 0 / 14 - loss: 0.00480 +step 10 / 14 - loss: 0.00547 +Eval begin... +step 0 / 4 - loss: 0.00085 +Eval samples: 428 +Epoch 22 / 30 +step 0 / 14 - loss: 0.00463 +step 10 / 14 - loss: 0.00429 +Eval begin... +step 0 / 4 - loss: 0.00088 +Eval samples: 428 +Epoch 23 / 30 +step 0 / 14 - loss: 0.00439 +step 10 / 14 - loss: 0.00575 +Eval begin... +step 0 / 4 - loss: 0.00302 +Eval samples: 428 +Epoch 24 / 30 +step 0 / 14 - loss: 0.00447 +step 10 / 14 - loss: 0.00492 +Eval begin... +step 0 / 4 - loss: 0.00134 +Eval samples: 428 +Epoch 25 / 30 +step 0 / 14 - loss: 0.00526 +step 10 / 14 - loss: 0.00549 +Eval begin... +step 0 / 4 - loss: 0.00141 +Eval samples: 428 +Epoch 26 / 30 +step 0 / 14 - loss: 0.00387 +step 10 / 14 - loss: 0.00477 +Eval begin... +step 0 / 4 - loss: 0.00243 +Eval samples: 428 +Epoch 27 / 30 +step 0 / 14 - loss: 0.00668 +step 10 / 14 - loss: 0.00480 +Eval begin... +step 0 / 4 - loss: 0.00094 +Eval samples: 428 +Epoch 28 / 30 +step 0 / 14 - loss: 0.00388 +step 10 / 14 - loss: 0.00392 +Eval begin... +step 0 / 4 - loss: 0.00090 +Eval samples: 428 +Epoch 29 / 30 +step 0 / 14 - loss: 0.00384 +step 10 / 14 - loss: 0.00503 +Eval begin... +step 0 / 4 - loss: 0.00099 +Eval samples: 428 +Epoch 30 / 30 +step 0 / 14 - loss: 0.00454 +step 10 / 14 - loss: 0.00398 +Eval begin... +step 0 / 4 - loss: 0.00083 +Eval samples: 428 +INFO 2022-04-02 23:34:34,617 launch.py:268] Local processes completed. +``` +多机多卡分布式任务：相对于单机多卡的分布式任务，多机多卡下不需要对代码有任何的更改，故不需要更改程序，只需要改变一下启动方式。这里对于多机的情况，我在曙光超算上开启了两个镜像，每个镜像申请了 2 个加速卡，开启之后首先使用 ifconfig 查看两个镜像下的 ip 地址，然后使用 ping 指令查看一下两个镜像能否相互 ping 通，然后分别在两个镜像下使用下面指令运行：（其中 ips 中的两个 ip 地址换成镜像中的 ip 地址） +```python +python -m paddle.distributed.launch --ips="192.168.0.1,192.168.0.2" --gpus 0,1 train_fleet_dygraph.py +``` +运行结果如下： +```python +----------- Configuration Arguments ----------- +gpus: 0,1 +heter_worker_num: None +heter_workers: +http_port: None +ips: 173.8.206.5,173.15.88.4 +log_dir: log +nproc_per_node: None +run_mode: None +server_num: None +servers: +training_script: train_multi_4.py +training_script_args: [] +worker_num: None +workers: +------------------------------------------------ +INFO 2022-04-03 16:58:50,318 launch.py:348] Run collective mode. gpu arguments:['--ips'], cuda count:2 +launch train in GPU mode! +INFO 2022-04-03 16:58:50,320 launch_utils.py:510] Local start 2 processes. First process distributed environment info (Only For Debug): + +=======================================================================================+ + | Distributed Envs Value | + +---------------------------------------------------------------------------------------+ + | PADDLE_TRAINER_ID 2 | + | PADDLE_CURRENT_ENDPOINT 173.15.88.4:6070 | + | PADDLE_TRAINERS_NUM 4 | + | PADDLE_TRAINER_ENDPOINTS ... .5:6071,173.15.88.4:6070,173.15.88.4:6071| + | PADDLE_RANK_IN_NODE 0 | + | PADDLE_LOCAL_DEVICE_IDS 0 | + | PADDLE_WORLD_DEVICE_IDS 0,1,0,1 | + | FLAGS_selected_gpus 0 | + | FLAGS_selected_accelerators 0 | + +=======================================================================================+ + +INFO 2022-04-03 16:58:50,321 launch_utils.py:514] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 +launch proc_id:226 idx:0 +launch proc_id:229 idx:1 +W0403 16:58:55.397342 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 1 times with reason: Connection refused retry after 0.5 seconds +W0403 16:58:55.913834 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 2 times with reason: Connection refused retry after 1 seconds +W0403 16:58:56.914870 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 3 times with reason: Connection refused retry after 1.5 seconds +W0403 16:58:58.419081 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 4 times with reason: Connection refused retry after 2 seconds +W0403 16:59:00.419550 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 5 times with reason: Connection refused retry after 2.5 seconds +I0403 16:59:00.498201 226 gen_comm_id_helper.cc:181] Server listening on: 173.15.88.4:6070 successful. +I0403 16:59:02.923162 342 nccl_context.cc:74] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 0 +I0403 16:59:02.922478 226 nccl_context.cc:74] init nccl context nranks: 4 local rank: 2 gpu id: 0 ring id: 0 +W0403 16:59:03.412863 226 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 90.0, Driver API Version: 321.0, Runtime API Version: 3.1 +W0403 16:59:03.413836 342 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 90.0, Driver API Version: 321.0, Runtime API Version: 3.1 +W0403 16:59:03.421044 226 device_context.cc:417] device: 0, MIOpen Version: 2.11.0 +W0403 16:59:03.431119 342 device_context.cc:417] device: 0, MIOpen Version: 2.11.0 +I0403 16:59:03.452879 342 nccl_context.cc:107] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 10 +I0403 16:59:03.452358 226 nccl_context.cc:107] init nccl context nranks: 4 local rank: 2 gpu id: 0 ring id: 10 +/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. +Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations + if data.dtype == np.object: +/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. +Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations + if data.dtype == np.object: +2022-04-03 16:59:03,590-INFO: [topology.py:152:__init__] HybridParallelInfo: rank_id: 0, dp_degree: 4, mp_degree: 1, pp_degree: 1, dp_group: [0, 1, 2, 3], mp_group: [0], pp_group: [0], check/clip group: [0] +2022-04-03 16:59:03,590-INFO: [topology.py:152:__init__] HybridParallelInfo: rank_id: 2, dp_degree: 4, mp_degree: 1, pp_degree: 1, dp_group: [0, 1, 2, 3], mp_group: [2], pp_group: [2], check/clip group: [2] +Epoch 1 / 30 +Epoch 1 / 30 +/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance. + "When training, we now always track global mean and variance.") +/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance. + "When training, we now always track global mean and variance.") +step 0 / 14 - loss: 0.85710step 0 / 14 - loss: 0.84205 + +step 10 / 14 - loss: 0.05992step 10 / 14 - loss: 0.05753 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.01912 +step 0 / 4 - loss: 0.01973 +Eval samples: 428 +Epoch 2 / 30 +Eval samples: 428 +Epoch 2 / 30 +step 0 / 14 - loss: 0.04044step 0 / 14 - loss: 0.03697 + +step 10 / 14 - loss: 0.02107 +step 10 / 14 - loss: 0.02008 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00549 +step 0 / 4 - loss: 0.00503 +Eval samples: 428 +Epoch 3 / 30 +Eval samples: 428 +Epoch 3 / 30 +step 0 / 14 - loss: 0.01858step 0 / 14 - loss: 0.02017 + +step 10 / 14 - loss: 0.01667 +step 10 / 14 - loss: 0.01475 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00300 +step 0 / 4 - loss: 0.00350 +Eval samples: 428 +Epoch 4 / 30 +Eval samples: 428 +Epoch 4 / 30 +step 0 / 14 - loss: 0.01376step 0 / 14 - loss: 0.01363 + +step 10 / 14 - loss: 0.01104 +step 10 / 14 - loss: 0.01042 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00240 +step 0 / 4 - loss: 0.00249 +Eval samples: 428 +Epoch 5 / 30 +Eval samples: 428 +Epoch 5 / 30 +step 0 / 14 - loss: 0.01193 +step 0 / 14 - loss: 0.01152 +step 10 / 14 - loss: 0.01055 +step 10 / 14 - loss: 0.01039 +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00166 +step 0 / 4 - loss: 0.00154 +Eval samples: 428 +Epoch 6 / 30 +Eval samples: 428 +Epoch 6 / 30 +step 0 / 14 - loss: 0.01019step 0 / 14 - loss: 0.00879 + +step 10 / 14 - loss: 0.00636 +step 10 / 14 - loss: 0.00875 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00257 +step 0 / 4 - loss: 0.00269 +Eval samples: 428 +Epoch 7 / 30 +Eval samples: 428 +Epoch 7 / 30 +step 0 / 14 - loss: 0.00884 +step 0 / 14 - loss: 0.00832 +step 10 / 14 - loss: 0.01013step 10 / 14 - loss: 0.00839 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00227 +step 0 / 4 - loss: 0.00179 +Eval samples: 428 +Epoch 8 / 30 +Eval samples: 428 +Epoch 8 / 30 +step 0 / 14 - loss: 0.00957step 0 / 14 - loss: 0.00895 + +step 10 / 14 - loss: 0.00707step 10 / 14 - loss: 0.00848 + +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00154 +step 0 / 4 - loss: 0.00165 +Eval samples: 428 +Epoch 9 / 30 +Eval samples: 428 +Epoch 9 / 30 +step 0 / 14 - loss: 0.00606 +step 0 / 14 - loss: 0.00653 +step 10 / 14 - loss: 0.00564 +step 10 / 14 - loss: 0.00885 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00180 +step 0 / 4 - loss: 0.00198 +Eval samples: 428 +Epoch 10 / 30 +Eval samples: 428 +Epoch 10 / 30 +step 0 / 14 - loss: 0.00697step 0 / 14 - loss: 0.00953 + +step 10 / 14 - loss: 0.00577 +step 10 / 14 - loss: 0.00713 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00132 +step 0 / 4 - loss: 0.00098 +Eval samples: 428 +Epoch 11 / 30 +Eval samples: 428 +Epoch 11 / 30 +step 0 / 14 - loss: 0.00611 +step 0 / 14 - loss: 0.00576 +step 10 / 14 - loss: 0.00705step 10 / 14 - loss: 0.00557 + +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00229 +step 0 / 4 - loss: 0.00296 +Eval samples: 428 +Epoch 12 / 30 +Eval samples: 428 +Epoch 12 / 30 +step 0 / 14 - loss: 0.00789step 0 / 14 - loss: 0.00599 + +step 10 / 14 - loss: 0.00526 +step 10 / 14 - loss: 0.00603 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00172 +step 0 / 4 - loss: 0.00214 +Eval samples: 428 +Epoch 13 / 30 +Eval samples: 428 +Epoch 13 / 30 +step 0 / 14 - loss: 0.00730 +step 0 / 14 - loss: 0.00736 +step 10 / 14 - loss: 0.00490step 10 / 14 - loss: 0.00524 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00129step 0 / 4 - loss: 0.00103 + +Eval samples: 428 +Epoch 14 / 30 +Eval samples: 428 +Epoch 14 / 30 +step 0 / 14 - loss: 0.00460step 0 / 14 - loss: 0.00474 + +step 10 / 14 - loss: 0.01072step 10 / 14 - loss: 0.01057 + +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00576 +step 0 / 4 - loss: 0.00567 +Eval samples: 428 +Epoch 15 / 30 +Eval samples: 428 +Epoch 15 / 30 +step 0 / 14 - loss: 0.00579 +step 0 / 14 - loss: 0.00819 +step 10 / 14 - loss: 0.00540step 10 / 14 - loss: 0.00567 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00103 +step 0 / 4 - loss: 0.00086 +Eval samples: 428 +Epoch 16 / 30 +Eval samples: 428 +Epoch 16 / 30 +step 0 / 14 - loss: 0.00486 +step 0 / 14 - loss: 0.00365 +step 10 / 14 - loss: 0.00576step 10 / 14 - loss: 0.00696 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00199 +step 0 / 4 - loss: 0.00195 +Eval samples: 428 +Epoch Eval samples: 17428 +/Epoch 3017 + / 30 +step 0 / 14 - loss: 0.00727 +step 0 / 14 - loss: 0.00616 +step 10 / 14 - loss: 0.00570step 10 / 14 - loss: 0.00670 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00239 +step 0 / 4 - loss: 0.00206 +Eval samples: 428 +Epoch 18 / 30 +Eval samples: 428 +Epoch 18 / 30 +step 0 / 14 - loss: 0.00725step 0 / 14 - loss: 0.00614 + +step 10 / 14 - loss: 0.00709 +step 10 / 14 - loss: 0.00581 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00101 +step 0 / 4 - loss: 0.00106 +Eval samples: Eval samples: 428428 + +Epoch Epoch 1919 // 3030 + +step 0 / 14 - loss: 0.00431 +step 0 / 14 - loss: 0.00360 +step 10 / 14 - loss: 0.00403 +step 10 / 14 - loss: 0.00461 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00208 +step 0 / 4 - loss: 0.00230 +Eval samples: 428 +Epoch 20 / 30 +Eval samples: 428 +Epoch 20 / 30 +step 0 / 14 - loss: 0.00542step 0 / 14 - loss: 0.00424 + +step 10 / 14 - loss: 0.00426 +step 10 / 14 - loss: 0.00520 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00098 +step 0 / 4 - loss: 0.00088 +Eval samples: 428 +Epoch 21 / 30 +Eval samples: 428 +Epoch 21 / 30 +step 0 / 14 - loss: 0.00452 +step 0 / 14 - loss: 0.00440 +step 10 / 14 - loss: 0.00489step 10 / 14 - loss: 0.00504 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00619 +step 0 / 4 - loss: 0.00630 +Eval samples: 428 +Epoch 22 / 30 +Eval samples: 428 +Epoch 22 / 30 +step 0 / 14 - loss: 0.00808 +step 0 / 14 - loss: 0.00936 +step 10 / 14 - loss: 0.00833step 10 / 14 - loss: 0.00734 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00144 +step 0 / 4 - loss: 0.00146 +Eval samples: 428 +Epoch 23 / 30 +Eval samples: 428 +Epoch 23 / 30 +step 0 / 14 - loss: 0.00359 +step 0 / 14 - loss: 0.00316 +step 10 / 14 - loss: 0.00343step 10 / 14 - loss: 0.00360 + +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00134 +step 0 / 4 - loss: 0.00156 +Eval samples: 428 +Epoch 24 / 30 +Eval samples: 428 +Epoch 24 / 30 +step 0 / 14 - loss: 0.00492 +step 0 / 14 - loss: 0.00398 +step 10 / 14 - loss: 0.00439step 10 / 14 - loss: 0.00321 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00180 +step 0 / 4 - loss: 0.00185 +Eval samples: 428 +Epoch 25 / 30 +Eval samples: 428 +Epoch 25 / 30 +step 0 / 14 - loss: 0.00396step 0 / 14 - loss: 0.00419 + +step 10 / 14 - loss: 0.00630step 10 / 14 - loss: 0.00398 + +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00308step 0 / 4 - loss: 0.00300 + +Eval samples: 428 +Epoch 26 / 30 +Eval samples: 428 +Epoch 26 / 30 +step 0 / 14 - loss: 0.00535 +step 0 / 14 - loss: 0.00487 +step 10 / 14 - loss: 0.00413step 10 / 14 - loss: 0.00418 + +Eval begin...Eval begin... + +step 0 / 4 - loss: 0.00072 +step 0 / 4 - loss: 0.00091 +Eval samples: 428 +Epoch 27 / 30 +Eval samples: 428 +Epoch 27 / 30 +step 0 / 14 - loss: 0.00315step 0 / 14 - loss: 0.00386 + +step 10 / 14 - loss: 0.00435step 10 / 14 - loss: 0.00286 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00116 +step 0 / 4 - loss: 0.00125 +Eval samples: 428 +Epoch 28 / 30 +Eval samples: 428 +Epoch 28 / 30 +step 0 / 14 - loss: 0.00396step 0 / 14 - loss: 0.00281 + +step 10 / 14 - loss: 0.00449step 10 / 14 - loss: 0.00416 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00104 +step 0 / 4 - loss: 0.00141 +Eval samples: 428 +Epoch 29 / 30 +Eval samples: 428 +Epoch 29 / 30 +step 0 / 14 - loss: 0.00542step 0 / 14 - loss: 0.00310 + +step 10 / 14 - loss: 0.00570 +step 10 / 14 - loss: 0.00425 +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00171 +step 0 / 4 - loss: 0.00193 +Eval samples: 428 +Epoch 30 / 30 +Eval samples: 428 +Epoch 30 / 30 +step 0 / 14 - loss: 0.00446step 0 / 14 - loss: 0.00518 + +step 10 / 14 - loss: 0.00387step 10 / 14 - loss: 0.00282 + +Eval begin... +Eval begin... +step 0 / 4 - loss: 0.00094 +step 0 / 4 - loss: 0.00079 +Eval samples: 428 +Eval samples: 428 +``` + +## 3.3 评估表格 +| | | | | | +| :------------ | ---------- | --------------- | ------ | ------| +| 序号 | 核心步骤 | 完成情况（成功/不成功） | 遇到问题 |解决方法（无法解决请注明）| +| 1 | 导入分布式训练所需要的依赖包 | 完成 | 无 | 无 | +| 2 | 初始化分布式环境 | 完成 | PaddlePaddle 安装有时候会有一些问题、NCCL 初始化有问题![图片](https://user-images.githubusercontent.com/35827074/165877509-b84f5846-b175-4ab9-8ae3-eef66ed09047.png) | 使用 export 设置一些安装的库的环境变量，上述问题是 rocm 版本问题，需要使用 rocm-4.0.1 版本。 修改 rocm 版本的方法为. module switch compiler/rocm/4.0.1，再就是导入超算上的一些环境变量 export NCCL_IB_HCA=mlx5_0 export NCCL_SOCKET_IFNAME=eno1 export NCCL_IB_DISABLE=0 | +| 3 | 设置分布式训练需要的优化器 | 完成 | 无 | 无 | +| 4 | 数据集拆分 | 完成 | 示例里面没有数据集的拆分案例，不会使用数据集的拆分；使用 DistributedBatchSampler 采样器之后 DataLoader 中无法指定 batchsize 以及 shuffle 参数 | 分析 paddle 的分布式 API 底层以及结合其他深度学习框架分析，发现了 DistributedBatchSampler API，然后分析其底层实现，发现可以应用；分析 DataLoader 底层的源码，发现在指定 batch_sampler 参数之后不能指定 batchsize、shuffle 以及 drop_last 参数，然后在 DistributedBatchSampler 构建采样器的过程中指定。分布式数据集拆分使用 DistributedBatchSampler，通过使用 DistributedBatchSampler 构建一个分布式的采样器，其会将数据平均划分到多个设备中，然后将其输入到 Dataloader 函数中，参数为 batch_sampler，案例的全部代码已经在附录中给出。关于拆分部分如下：train_sampler = DistributedBatchSampler(train_dataset, 32, shuffle=True) train_loader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=2) val_sampler = DistributedBatchSampler(val_dataset, 32) val_loader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=2) | +| 5 | 构建训练代码 | 完成 | 无 | 无 | +| 6 | 单机多卡分布式训练 | 完成 | 在曙光超算上使用 SBATCH 作业提交方式时有环境的问题 | 申请 4 个 DCU，使用镜像的方式进行实现 | +| 7 | 多机多卡分布式训练 | 完成 | 无 | 注意再进行多机多卡时先要两个机器之间互相 ping 一下 | + +* 总结：上述单机转为分布式的过程中，总体来说感觉还是可以的，动态图下 paddle 单机转为分布式的代码还是比较方便的，也有一些官网上的参考文档用于学习，但是有一些是在其参考文档中没有介绍的，例如数据集的拆分等这些需要自己去思考。 + +# 4、PyTorch 单机与分布式： +## 4.1、PyTorch 单机 + +PyTorch 单机下的流程和 Paddle 单机下的流程基本上是相似的，采用了和 Paddle 单机下相同的模型，相同的优化器与损失函数，过程如下： +- 1、导入相关库 +- 2、构建数据集 +- 3、定义模型 +- 4、构建损失函数与优化器 +- 5、训练模型 +- 6、预测 + +## 4.2、PyTorch 分布式 + +PyTorch 单机转为分布式的具体流程如下： +- 1、导入分布式所需要的依赖包 +```python +import torch.distributed as dist +``` +- 2、初始化分布式环境，需要指定一下通信后端（我采用的是 NCCL），初始化方法（我采用的是 env 初始化），当前进程号以及总的进程数量。 +```python +rank = int(os.environ["RANK"]) +world_size = int(os.environ['WORLD_SIZE']) +gpu = int(os.environ['LOCAL_RANK']) +device = gpu +torch.cuda.set_device(gpu) +dist.init_process_group(backend="nccl", world_size=world_size, rank=rank) +``` +3、构建模型，这里需要使用 torch.nn.parallel.DistributedDataParallel 构建其分布式的模型，这里没有采用 Dataparallel API，是因为其效率相对于 DistributedDataParallel 比较低。 +```python +model = FaceNet(num_keypoints=15).to(device) +model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu]) +``` +4、构建优化器与损失函数 +优化器采用 SGD，指定学习率与动量等参数。 +```python +optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.0001, momentum=0.9) +``` +损失函数采用均方误差损失函数。 +```python +criterion = torch.nn.MSELoss() +``` +5、数据集的拆分，首先使用 DistributedSampler 构建分布式数据集的拆分，这里可以指定一下是否需要进行 shuffle 以及 drop_last 等参数，然后使用 DataLoader 进行封装。 +```python +train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True) +train_loader = torch.utils.data.DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size) +val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False) +val_loader = torch.utils.data.DataLoader(val_dataset, sampler=val_sampler, batch_size=batch_size) +``` +6、构建训练代码，这里需要使用 train_sampler.set_epoch(epoch)设置一下，因为在 train_sampler 中使用了 shuffle 这个参数，而看其源码可以发现其依靠 self.epoch 这个参数进行随机种子的设置，所以需要在每个 epoch 训练时设置一下采样器的 self.epoch 这个参数，即通过 train_sampler.set_epoch(epoch)进行设置。训练代码如下，每一个 epoch 训练完进行 val 的验证评估： +```python +for epoch in range(total_epoch): + model.train() + train_sampler.set_epoch(epoch) + for batch_id, data in enumerate(train_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + if batch_id % 10 == 0: + print("step %d / %d - loss: %.5f" % (batch_id, len(train_loader), loss.item())) + model.eval() + print("Eval begin...") + for batch_id, data in enumerate(val_loader): + inputs, labels = data + inputs = inputs.to(device) + labels = labels.to(device) + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, labels) + if batch_id % 10 == 0: + print("step %d / %d - loss: %.5f" % (batch_id, len(val_loader), loss.item())) + print("Eval samples: ", len(val_dataset)) +``` +7、启动分布式任务 +PyTorch 分布式下两种启动方式，我选择的是和 paddle 类似的一种方式即通过 torch.distributed.launch 进行启动，启动方式如下： +```bash +python -m torch.distributed.launch --nproc_per_node=4 train_multi.py +``` +# 4、对比分析 + +下面对 PaddlePaddle 与 PyTorch 单机转为分布式进行对比性分析： +## 相似点： +PaddlePaddle 与 PyTorch 单机转为分布式的流程基本上是相似的，基本上遵循如下流程：导入分布式相关的库、初始化分布式环境、构建分布式的模型、构建优化器与损失函数、同时进行分布式数据集的拆分，最后构建训练代码，其整个流程都比较相似，相对来说 PaddlePaddle 与 PyTorch 单机转为分布式都是比较方便的。 +## 不同点： +- 1、PaddlePaddle 内部有许多封装好的类，例如 paddle.Model 类，其内部封装了好多函数，例如 train_batch/fit 等函数，还加入了一些回调函数例如 EarlyStopping 等，可以比较方便地进行训练、测试的过程，比较容易使用。 +- 2、对于单机转为分布式的过程，如果对数据集进行 shuffle 打乱时，PyTorch 需要在每个 epoch 训练开始时调用 train_sampler.set_epoch 函数即设置一下 shuffle 打乱的种子，但是 PaddlePaddle 如果对数据集进行 shuffle 打乱时，可以选择并不需要设置，因为其内部在每次打乱时会将 self.epoch 进行加一的操作，即自动改变了其数据打乱的种子，使用起来更加方便。 +- 3、从使用方面来说，PaddlePaddle 的分布式初始化有时候会报错有时候能使用，其环境用起来感觉不太稳定，PyTorch 的分布式使用起来相对比较稳定，其初始化环境等功能实现都比较稳定。 +- 4、从官方文档来说，PaddlePaddle 的分布式示例文档中感觉不太完善，例如 DistributedSampler 等的 API 没有在分布式示例文档中展现，paddle.Model 等 API 没有找到相关 API 文档的介绍；PyTorch 的分布式示例文档相对来说比较完善，包括其示例以及 API 的使用以及分布式通信的相关 API 都有其文档介绍。 + +# 5、附录 +## 单机示例转为分布式的代码 +```python +import numpy as np +import pandas as pd + +import paddle +from paddle.io import Dataset +from paddle.vision.transforms import transforms +from paddle.vision.models import resnet18 +# 导入必要分布式训练的依赖包 +from paddle.distributed import fleet +from paddle.io import Dataset, BatchSampler, DataLoader, DistributedBatchSampler + +# 数据文件的路径 +# training.csv: 包含了用于训练的人脸关键点坐标和图像。 +# test.csv: 包含了用于测试的人脸关键点图像, 没有标注关键点坐标。 +# IdLookupTable.csv: 测试集关键点的位置的对应名称。 +Train_Dir = './data/data60/training.csv' +Test_Dir = './data/data60/test.csv' +lookid_dir = './data/data60/IdLookupTable.csv' + + +class ImgTransforms: + """ + 图像预处理工具，用于将图像进行升维(96, 96) => (96, 96, 3)， + 并对图像的维度进行转换从 HWC 变为 CHW + """ + + def __init__(self, fmt): + self.format = fmt + + def __call__(self, img): + if len(img.shape) == 2: + img = np.expand_dims(img, axis=2) + img = img.transpose(self.format) + + if img.shape[0] == 1: + img = np.repeat(img, 3, axis=0) + return img + + +class FaceDataset(Dataset): + def __init__(self, data_path, mode='train', val_split=0.2): + self.mode = mode + assert self.mode in ['train', 'val', 'test'], \ + "mode should be 'train' or 'test', but got {}".format(self.mode) + # 读取数据的路径 + self.data_source = pd.read_csv(data_path) + # 清洗数据, 数据集中有很多样本只标注了部分关键点, 这里有两种策略 + # 第一种, 将未标注的位置从上一个样本对应的关键点复制过来 + # self.data_source.fillna(method = 'ffill',inplace = True) + # 第二种, 将包含有未标注的样本从数据集中移除 + self.data_source.dropna(how="any", inplace=True) + self.data_label_all = self.data_source.drop('Image', axis=1) + + # 划分训练集和验证集合 + if self.mode in ['train', 'val']: + np.random.seed(43) + data_len = len(self.data_source) + # 随机划分 + shuffled_indices = np.random.permutation(data_len) + # 顺序划分 + # shuffled_indices = np.arange(data_len) + self.shuffled_indices = shuffled_indices + val_set_size = int(data_len * val_split) + if self.mode == 'val': + val_indices = shuffled_indices[:val_set_size] + self.data_img = self.data_source.reindex().iloc[val_indices] + self.data_label = self.data_label_all.reindex().iloc[val_indices] + elif self.mode == 'train': + train_indices = shuffled_indices[val_set_size:] + self.data_img = self.data_source.reindex().iloc[train_indices] + self.data_label = self.data_label_all.reindex().iloc[train_indices] + elif self.mode == 'test': + self.data_img = self.data_source + self.data_label = self.data_label_all + + self.transforms = transforms.Compose([ + ImgTransforms((2, 0, 1)) + ]) + + # 每次迭代时返回数据和对应的标签 + def __getitem__(self, idx): + img = self.data_img['Image'].iloc[idx].split(' ') + img = ['0' if x == '' else x for x in img] + img = np.array(img, dtype='float32').reshape(96, 96) + # 图像预处理操作 + img = self.transforms(img) + label = np.array(self.data_label.iloc[idx, :], dtype='float32') / 96 + return img, label + + # 返回整个数据集的总数 + def __len__(self): + return len(self.data_img) + +# 模型的定义 +# 对应 30 维度 +class FaceNet(paddle.nn.Layer): + def __init__(self, num_keypoints, pretrained=False): + super(FaceNet, self).__init__() + self.backbone = resnet18(pretrained) + self.outLayer1 = paddle.nn.Sequential( + paddle.nn.Linear(1000, 512), + paddle.nn.ReLU(), + paddle.nn.Dropout(0.1)) + self.outLayer2 = paddle.nn.Linear(512, num_keypoints*2) + + def forward(self, inputs): + out = self.backbone(inputs) + out = self.outLayer1(out) + out = self.outLayer2(out) + return out + + +def main(): + # 初始化 Fleet 环境 + fleet.init(is_collective=True) + # 训练数据集和验证数据集 + train_dataset = FaceDataset(Train_Dir, mode='train') + val_dataset = FaceDataset(Train_Dir, mode='val') + # 测试数据集 + test_dataset = FaceDataset(Test_Dir, mode='test') + # 初始化模型 + model = FaceNet(num_keypoints=15) + # 优化器的设置 + optim = paddle.optimizer.Adam(learning_rate=1e-3, parameters=model.parameters()) + # 构建分布式优化器 + optim = fleet.distributed_optimizer(optim) + # 通过 Fleet API 获取分布式 model，用于支持分布式训练 + model = fleet.distributed_model(model) + # 数据集的拆分 构建分布式数据集 + train_sampler = DistributedBatchSampler(train_dataset, 32, shuffle=True) + train_loader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=2) + val_sampler = DistributedBatchSampler(val_dataset, 32) + val_loader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=2) + epoch = 30 + for eop in range(epoch): + # train_sampler.set_epoch(eop) + # 设置为训练模式 + model.train() + print("Epoch ", eop + 1, "/", epoch) + for batch_id, data in enumerate(train_loader()): + img, label = data + label.stop_gradient = True + # 前向传播 + out = model(img) + # 均方损失函数 + loss = paddle.nn.functional.mse_loss(input=out, label=label) + loss_data = loss.numpy() + # 反向传播 + loss.backward() + optim.step() + model.clear_gradients() + if batch_id % 10 == 0: + print("step %d / %d - loss: %.5f" % (batch_id, len(train_loader), loss_data)) + model.eval() + print("Eval begin...") + for batch_id, data in enumerate(val_loader()): + img, label = data + label.stop_gradient = True + # 前向传播 + out = model(img) + # 均方损失函数 + loss = paddle.nn.functional.mse_loss(input=out, label=label) + loss_data = loss.numpy() + if batch_id % 10 == 0: + print("step %d / %d - loss: %.5f" % (batch_id, len(val_loader), loss_data)) + print("Eval samples: ", len(val_dataset)) + + +if __name__ == "__main__": + main() +``` diff --git "a/docs/eval/\343\200\220Hackathon No.69\343\200\221PR.md" "b/docs/eval/\343\200\220Hackathon No.69\343\200\221PR.md" new file mode 100644 index 00000000000..a631f9bc315 --- /dev/null +++ "b/docs/eval/\343\200\220Hackathon No.69\343\200\221PR.md" @@ -0,0 +1,1095 @@ +# 飞桨动态图单机训练功能评估报告 + +| 领域 | 飞桨动态图单机训练功能评估报告 | +| ------------ | ------------------------------ | +| 提交作者 | 王源 袁闯闯 | +| 提交时间 | 2022-05-03 | +| 版本号 | V1.0 | +| 依赖飞桨版本 | paddlepaddle-gpu==2.2 | +| 文件名 | 【Hackathon No.69】 PR.md | + + +# 一、摘要 + +相关背景：飞桨框架于 2.0 正式版全面支持了动态图训练，并在 2.1、2.2 两个大版本中不断新增了 API 以及大幅增强了训练功能。希望有人对于飞桨框架动态图下单机训练功能整体的使用感受，可以与其他深度学习框架做功能对比，包括 API、Tensor 索引、NumPy Compatibility、报错信息提示、训练性能、以及各种 trick 的用法等，并产出一份对应的评估报告。 + +本评估方案将从以下几个方面对 paddle 动态图单机训练功能进行体验评估： + +1、环境配置及开启动态图模式 + +2、API 使用及对比 + +调用高层 API:如：paddle.Model、paddle.vision，与 pytorch 框架做对比。并在 LeNet、ResNet 等网络模型或模型自己组网（Sequential 组网、SubClass 组网）训练中进行评估。 + +3、Tensor 索引 + +在模型训练中体验了 Tensor 在数据传递过程中的表现（如：了解索引和 其切片规则、访问与修改 Tensor、逻辑相关函数重写规则），并体验了使用指南里有关 Tensor 的所有基本操作。 + +4、NumPy 兼容性分析及对比 + +在动态图模型代码中，所有与组网相关的 numpy 操作都必须用 paddle 的 API 重新实现，所以在模型训练过程中体验 Paddle.API 来感受对比 PyTorch 的表现；分析了 Tensor 兼容 Numpy 数组的同时，优先使用 Tensor 的两种场景。 + +5、动态图单机训练 + +体验控制流和共享权重的使用效果，然后在数据集定义、加载和数据预处理、数据增强方面感受与 PyTorch 使用的区别，最后通过 LeNet 举例说明训练结果，并进行了对比分析 + +6、各种 trick 的用法体验 + +7、报错汇总 + +# 二、环境配置及开启动态图模式 + +本次训练评估在个人电脑上进行： + +| 名称 | 参数 | +| :------: | :------------------------------------------: | +| CPU | Intel(R)Core(TM)i5-7200U CPU @2.50GHz | +| 内存 | 12GB DDR4 | +| GPU | NVIDIA GeForce 940MX | +| 系统平台 | Window 10 家庭中文版（64 位） | +| 软件环境 | Paddle2.2、 PyTorch3.8、Cuda 10.1、Anaconda3 | + +Paddle 环境安装参考： https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/windows-pip.html + +安装 CPU 版本时候使用到了清华镜像源：pip install -i https://pypi.tuna.tsinghua.edu.cn/simple paddlepaddle + +GPU 版本：python -m pip install paddlepaddle-gpu==2.2.2.post101 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html + +Paddle 与 PyTorch 环境配置使用对比： 在单机安装中，都安装在了 conda 环境，Paddle 安装比较顺利，直接按照文档安装即可，与 PyTorch 安装没有太大区别，单机测试稳定性也都比较良好。 + + + +# 三、API 使用及对比 + +## 1、从 PaddlePaddle 的各个 API 目录来对比分析 + +首先，基础操作类、组网类、Loss 类、工具类、视觉类这五大类从映射 PyTorch 上看，在单机训练中可以满足训练及预测所需的 API 使用类别。 + +其次，在单机模型训练中对比了一下主要 API 的使用 + +| 名称 | PaddlePaddle | PyTorch | +| ------- | ----------------------------- | ------------------------------ | +| layer | nn.Layer | nn.Module | +| 各种层 | nn.layer2D(即 paddle 使用大写 D) | nn.layer2d(即 PyTorch 使用小写 d) | +| flatten | nn.Flatten | var.view(var.size(0), -1) | +| concat | paddle.concat | torch.cat | +| optim | paddle.optimizer | torch.optim | + +## 2、从具体 API 的参数设计差异作对比分析 + +通过训练和测试 Paddle 的动态图单机模型，就个人体验而言，对模型中常用到的一些 API 作简要分析，如 paddle.to_tensor，paddle.save,paddle.load,paddle.nn.Conv2D , paddle.nn.Linear, paddle.nn.CrossEntropyLoss , paddle.io.DataLoader + +### 2.1 基础操作类 API + +```python +#paddle.to_tensor +paddle.to_tensor(data, + dtype=None, + place=None, + stop_gradient=True) +#torch.tensor +torch.tensor(data, + dtype=None, + device=None, + requires_grad=False, + pin_memory=False) +``` + +在 paddle.to_tensor 中，stop_gradient 表示是否阻断梯度传导，PyTorch 的 requires_grad 表示是否不阻断梯度传导。 + +在 torch.tensor 中，pin_memeory 表示是否使用锁页内存，而 PaddlePaddle 却无此参数。 + +------ + +```python +#paddle.load +paddle.load(path, **configs) + +#torch.load +torch.load(f, + map_location=None, + pickle_module=pickle, + **pickle_load_args) +``` + +在 torch.load 中， pickle_module 表示用于 unpickling 元数据和对象的模块，PaddlePaddle 无此参数。 map_location 表示加载模型的位置，PaddlePaddle 无此参数。 + +在加载内容上，PyTorch 可以加载 torch.Tensor、torch.nn.Module、优化器等多个类型的数据。 +PaddlePaddle 只能加载 paddle.nn.Layer、优化器这两个类型的数据，这方面 PyTorch 更优一些。 + +------ + +```python +#paddle.save +paddle.save(obj, path, pickle_protocol=2) + +#torch.save +torch.save(obj, + f, + pickle_module=pickle, + pickle_protocol=2) +``` + +在 paddle.save 中， path 表示存储的路径，这一点比 PyTorch 的 f 更为清晰一些。 + +在 torch.save 中， pickle_module 表示用于 pickling 元数据和对象的模块，PaddlePaddle 无此参数。 + +还有在存储内容上，跟 paddle.load 情况类似，PaddlePaddle 只能存储 paddle.nn.Layer、优化器这两个类型的数据，个人觉得这方面 PaddlePaddle 有待加强。 + +------ + +### 2.2 组网类 API + +```python +#paddle.nn.Conv2D +paddle.nn.Conv2D(in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + padding_mode='zeros', + weight_attr=None, + bias_attr=None, + data_format='NCHW') +#torch.nn.Conv2d +torch.nn.Conv2d(in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros') +``` + +在 paddle.nn.Conv2D 中，PaddlePaddle 支持 NCHW 和 NHWC 两种格式的输入（通过 data_format 设置）。而 PyTorch 只支持 NCHW 的输入，这一点 PaddlePaddle 更优一些。 + +------ + +```python +#paddle.nn.Linear +paddle.nn.Linear(in_features, out_features, weight_attr=None, bias_attr=None, name=None) +#torch.nn.Linear +torch.nn.Linear(in_features, out_features, bias=True) +``` + +在 paddle.nn.Linear 中，weight_attr/bias_attr 默认使用默认的权重/偏置参数属性，否则为指定的权重/偏置参数属性，而 PyTorch 的 bias 默认为 True，表示使用可更新的偏置参数。需要注意的是在 PaddlePaddle 中，当 bias_attr 设置为 bool 类型与 PyTorch 的作用一致。 + +------ + +### 2.3 Loss 类 API + +```python +#paddle.nn.CrossEntropyLoss +paddle.nn.CrossEntropyLoss(weight=None, + ignore_index=-100, + reduction='mean', + soft_label=False, + axis=-1, + use_softmax=True, + name=None) +#torch.nn.CrossEntropyLoss +torch.nn.CrossEntropyLoss(weight=None, + size_average=None, + ignore_index=-100, + reduce=None, + reduction='mean') +``` + +在 paddle.nn.CrossEntropyLoss 中， use_softmax 表示在使用交叉熵之前是否计算 softmax，PyTorch 无此参数；soft_label 指明 label 是否为软标签，PyTorch 无此参数；而 axis 表示进行 softmax 计算的维度索引，PyTorch 无此参数。 在这个 API 中，个人感觉 PaddlePaddle 的表现优于 PyTorch。 + +------ + +### 2.4 工具类 API + +```python +#paddle.io.DataLoader +paddle.io.DataLoader(dataset, + feed_list=None, + places=None, + return_list=False, + batch_sampler=None, + batch_size=1, + shuffle=False, + drop_last=False, + collate_fn=None, + num_workers=0, + use_buffer_reader=True, + use_shared_memory=False, + timeout=0, + worker_init_fn=None) +#torch.utils.data.DataLoader +torch.utils.data.DataLoader(dataset, + batch_size=1, + shuffle=False, + sampler=None, + batch_sampler=None, + num_workers=0, + collate_fn=None, + pin_memory=False, + drop_last=False, + timeout=0, + worker_init_fn=None, + multiprocessing_context=None, + generator=None, + prefetch_factor=2, + persistent_workers=False) +``` + +在 paddle.io.DataLoader 中， feed_list 表示 feed 变量列表，PyTorch 无此参数。 use_shared_memory 表示是否使用共享内存来提升子进程将数据放入进程间队列的速度，PyTorch 无此参数。 + +在 torch.utils.data.DataLoader 中，prefetch_factor 表示每个 worker 预先加载的数据数量，PaddlePaddle 无此参数；还有就是 PyTorch 可通过设置 sampler 自定义数据采集器，PaddlePaddle 只能自定义一个 DataLoader 来实现该功能，会有些繁琐。总的来说，这部分 PyTorch 的体验更好一些。 + +------ + +​ 从整体的 API 使用上，感觉 paddle 升级后的 paddle.xxx （例如：paddle.device paddle.nn paddle.vision ）比之前的 padddle.fluid.xxx 好用很多，还有就是新增加的高层 API 个人比较喜欢，一是对初学者比较友好、易用，二是对于开发者可以节省代码量，更简洁直观一些，在（六、动态图单机训练）中进行了代码展示和对比分析。 + +与 PyTorch 相比，基础 API 的结构和调用没有太大区别，但是在速度上，paddle 的基础 API 会更快一点，如果是利用了 paddle 高层 API，速度会快很多，在同样 5 次 epoch 的情况下，LeNet 训练高层 API 用 38s 左右,基础 API 得用将近两分钟，所以用高层 API 能减少大约三分之二的训练时间。 + +总体来说，使用像 paddle.Model、paddle.vision 这样的高级 API 进行封装调用，使用体验比较好，个人感觉在以后深度学习模型普遍使用时，高层 API 会更受欢迎，也会成为模型训练测试中更为流行的一种方法。 + + + +# 四、Tensor 索引 + +在了解 Paddle 的 Tensor 索引和其切片规则以及逻辑相关函数重写规则等内容后，结合指南内容（ https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.html#id1 ）和模型训练过程中的 Tensor 索引使用，共有以下几点体验总结： + +**一、Paddle 可以使用静态数组索引；不可以使用 tensor 索引。** +示例：广播 (broadcasting) +1.每个张量至少为一维张量 + +2.从后往前比较张量的形状，当前维度的大小要么相等，要么其中一个等于一，要么其中一个不存在 + +```python +import paddle +x = paddle.ones((2, 3, 4)) +y = paddle.ones((2, 3, 4)) +# 两个张量 形状一致，可以广播 +z = x + y +print(z.shape) +# [2, 3, 4] + +x = paddle.ones((2, 3, 1, 5)) +y = paddle.ones((3, 4, 1)) +# 从后向前依次比较： +# 第一次：y 的维度大小是 1 +# 第二次：x 的维度大小是 1 +# 第三次：x 和 y 的维度大小相等 +# 第四次：y 的维度不存在 +# 所以 x 和 y 是可以广播的 +z = x + y +print(z.shape) +# [2, 3, 4, 5] + +# 相反 +x = paddle.ones((2, 3, 4)) +y = paddle.ones((2, 3, 6)) +# 此时 x 和 y 是不可广播的，因为第一次比较 4 不等于 6 +# z = x + y +# InvalidArgumentError: Broadcast dimension mismatch. +``` + +**二、两个张量进行广播语义后的结果张量的形状计算规则如下：** + +1.如果两个张量的形状的长度不一致，那么需要在较小形状长度的矩阵向前添加 1，直到两个张量的形状长度相等。 + +2.保证两个张量形状相等之后，每个维度上的结果维度就是当前维度上较大的那个。 + +```python +import paddle + +x = paddle.ones((2, 1, 4)) +y = paddle.ones((3, 1)) +z = x + y +print(z.shape) +# z 的形状: [2,3,4] + + +x = paddle.ones((2, 1, 4)) +y = paddle.ones((3, 2)) +# z = x + y +# ValueError: (InvalidArgument) Broadcast dimension mismatch. +``` + +**三、Paddle 目前支持的 Tensor 索引规则：** + +**Paddle 目前支持的 Tensor 索引状态：** + +1、基于 0-n 的下标进⾏索引 +2、如果下标为负数，则从尾部开始 +3、通过冒号 : 分隔切⽚参数 start:stop:step 来进⾏切⽚操作，其中 start、stop、step 均可缺省 + +示例 1：索引 + +```python +ndim_1_tensor = paddle.to_tensor([0, 1, 2, 3, 4, 5, 6, 7, 8]) +print("最初的 Tensor: ", ndim_1_tensor.numpy()) +print("取⾸端元素:", ndim_1_tensor[0].numpy()) +print("取末端元素:", ndim_1_tensor[-1].numpy()) +print("取所有元素:", ndim_1_tensor[:].numpy()) +print("取索引 3 之前的所有元素:", ndim_1_tensor[:3].numpy()) +print("取从索引 6 开始的所有元素:", ndim_1_tensor[6:].numpy()) +print("取从索引 3 开始到索引 6 之前的所有元素:", ndim_1_tensor[3:6].numpy()) +print("间隔 3 取所有元素:", ndim_1_tensor[::3].numpy()) +print("逆序取所有元素:", ndim_1_tensor[::-1].numpy()) +``` + +部分运⾏结果如下： + +```python +First element: [0] +Last element: [8] +All element: [0 1 2 3 4 5 6 7 8] +Before 3: [0 1 2] +From 6 to the end: [6 7 8] +From 3 to 6: [3 4 5] +Interval of 3: [0 3 6] +Reverse: [8 7 6 5 4 3 2 1 0] +``` + +**Paddle 目前不支持的 Tensor 索引状态：** + +示例 1：不能维度直接赋值 + +```python +#报错： +TypeError: 'paddle.fluid.libpaddle.VarBase' object does not support item assignment +#代码如下： +# pytorch code +Pred_boxes[:, 0] = pred_ctr_x - 0.5 * pred_w +pred_boxes[:, 1] = pred_ctr_y - 0.5 * pred_h +pred_boxes[:, 2] = pred_ctr_x + 0.5 * pred_w +pred_boxes[:, 3] = pred_ctr_y + 0.5 * pred_h + +# paddlepaddle code +pred_boxes = paddle.layers.concat([ + pred_ctr_x - 0.5 * pred_w, + pred_ctr_y - 0.5 * pred_h, + pred_ctr_x + 0.5 * pred_w, + pred_ctr_y + 0.5 * pred_h +]) + + +#维度报错: + +too many indices (3) for tensor of dimension 2 +#代码如下： +# pytorch code +bbox_x[bind, :, np.newaxis ] +# paddlepaddle code +paddle.layers.reshape(bbox_x[bind, :], [1, -1, 1]) +``` + +示例 2： tensor 的值不能直接利用 + +报错：paddlepaddle 中的 value 不能直接拿出来用。 + +```python +TypeError: The type of 'shape' in reshape must be list[int] or tuple(int) in + Dygraph mode, but received , which contains Variable. +#错误代码：其中 stack_size, feat_size 为 tensor。 + +#代码如下： +# paddlepaddle code +shift_x1 = paddle.layers.reshape(paddle.dygraph.to_variable(shift_x1), [1, stack_size, feat_size[1]]) + +``` + +改进加入 + +```python +# paddlepaddle code +stack_size = stack_size.numpy() +feat_size = feat_size.numpy() +``` + +**四、Tensor 索引整体体验** + +感觉在通过索引或切片修改 Tensor 的整体过程有些冗余，稳定性也会下降。虽然使用指南里说明了修改会导致原值不会被保存，可能会给梯度计算引入风险 ，但是在这点上个人感觉 PyTorch 的体验要好于 Paddle。 + +总的来说，在模型训练中利用 Tensor 加载数据集等操作上 PyTorch 与 Paddle 的体验并没有太大区别，但整体的感觉 PyTorch 的 Tensor 索引更好一些，个人感觉 Paddle 在修改 Tensor 的部分上可以增加一些文档说明。 + +**文档序号错误小提醒：** + + https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#tensor 中的“使用 Tensor 概念表示数据”下的序号应为 1、2、；文档中为两个 1、1、。 + +# 五、NumPy 兼容性分析及对比 + +NumPy 在 Paddle 的体验，感觉和 PyTorch 的体验并无区别，但是在阅读使用文档时的体验感较好，内容叙述很详细 （文档链接：https://www.paddlepaddle.org.cn/tutorials/projectdetail/3466356 ） + +**1、关于 numpy API 的重写** + +在 Paddle 动态图单机训练中，所有与组网相关的 numpy 操作都必须用 paddle 的 API 重新实现 ，这一点个人认为需要注意，因为在习惯使用 PyTorch 代码逻辑时，转为 PaddlePaddle 容易出错，下面举例说明： + +```python +#下述样例需要将 forward 中的所有的 numpy 操作都转为 Paddle API： +def forward(self, x): + out = self.linear(x) # [bs, 3] + + # 以下将 tensor 转为了 numpy 进行一系列操作 + x_data = x.numpy().astype('float32') # [bs, 10] + weight = np.random.randn([10,3]) + mask = paddle.to_tensor(x_data * weight) # 此处又转回了 Tensor + + out = out * mask + return out +``` + +注：由于在动态图模型代码中的 numpy 相关的操作不可以转为静态图，所以在动态图单机训练时候，只要与组网相关的 numpy 操作用 paddle 的 API 重新实现即可，所以在 numpy API 的重写部分，记住以上区别可以防止 Segment Fault 等错误的产生。 + +**2、关于 Tensor 操作的支持** + +在动态图单机训练中，感觉 Paddle 的 Tensor 高度兼容 Numpy 数组（array），发现增加了很多适用于深度学习任务的参数和方法，如反向计算梯度，更灵活的指定运行硬件 ，还有就是 Paddle 的 Tensor 可以与 Numpy 的数组方便的互转 ，比如以下代码展示： + +```python +import paddle +import numpy as np + +tensor_to_convert = paddle.to_tensor([1.,2.]) + +#通过 Tensor.numpy() 方法，将 Tensor 转化为 Numpy 数组 +tensor_to_convert.numpy() + +#通过 paddle.to_tensor() 方法，将 Numpy 数组 转化为 Tensor +tensor_temp = paddle.to_tensor(np.array([1.0, 2.0])) +``` + +**3、numpy 与 tensor 的转换补充** + +numpy 操作多样, 简单. 但网络前向只能是 tensor 类型, 各有优势, 所以需要相互转换补充. + +```python +# convert Tensor x of torch to array y of numpy: +y = x.numpy(); + +# convert array x of numpy to Tensor y of torch: +y = torch.from_numpy(x) + +# 先将数据转换成 Tensor, 再使用 CUDA 函数来将 Tensor 移动到 GPU 上加速 +如果想把 CUDA tensor 格式的数据改成 numpy 时，需要先将其转换成 cpu float-tensor 随后再转到 numpy 格式。 +x_np = x.data.numpy() + +# 改为： + +x_np = x.data.cpu().numpy() + +# 或者兼容上面两者的方式 +x_np = x.detach().cpu().numpy() if x.requires_grad else x.cpu().numpy() + +``` + +**整体体验：** + +感觉对于刚使用 Paddle 的新手，这部分需要注意的就是 Paddle 的 Tensor 虽然可以与 Numpy 的数组方便的互相转换 ，但是有两个场景优先使用 Paddle 的 Tensor 比较好: + +- 场景一：在组网程序中，对网络中向量的处理，务必使用 Tensor，而不建议转成 Numpy 的数组。如果在组网过程中转成 Numpy 的数组，并使用 Numpy 的函数会拖慢整体性能； +- 场景二：在数据处理和模型后处理等场景，建议优先使用 Tensor，主要是飞桨为 AI 硬件做了大量的适配和性能优化工作，部分情况下会获得更好的使用体验和性能。 + +建议：这两个场景内容可以增加一些实例，可能会使新手在这部分的理解更为透彻。 + +总体来说：Tensor 与 Numpy 数组的兼容与转换，Paddle 体验更好一点，兼容性上与 PyTorch 感觉没区别，但是 Paddle 的兼容转换处理上更具有一些前瞻性。 + +# 六、动态图单机训练 + +（1）使用 PyTorch 完成一个图像分类的动态图单机训练例子（MNIST 数据集） + +```python +import torch +from torch import nn +from net import LeNet +from torch.optim import lr_scheduler +from torchvision import datasets, transforms + +data_transform = transforms.Compose([ + transforms.ToTensor() # 仅对数据做转换为 tensor 格式操作 +]) + +# 加载训练数据集 +train_dataset = datasets.MNIST(root='./data', train=True, transform=data_transform, download=True) +# 给训练集创建一个数据集加载器 +train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=16, shuffle=True) +# 加载测试数据集 +test_dataset = datasets.MNIST(root='./data', train=False, transform=data_transform, download=True) +# 给测试集创建一个数据集加载器 +test_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=16, shuffle=True) + +# 如果显卡可用，则用显卡进行训练 +device = "cuda" if torch.cuda.is_available() else 'cpu' + +# 调用 net 里定义的模型，如果 GPU 可用则将模型转到 GPU +model = LeNet().to(device) + +# 定义损失函数（交叉熵损失） +loss_fn = nn.CrossEntropyLoss() + +# 定义优化器（SGD：随机梯度下降） +optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9) + +# 学习率每隔 10 个 epoch 变为原来的 0.1 +lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) + +# 定义训练函数 +def train(dataloader, model, loss_fn, optimizer): + loss, current, n = 0.0, 0.0, 0 + for batch, (X, y) in enumerate(dataloader): + # 前向传播 + X, y = X.to(device), y.to(device) + output = model(X) + cur_loss = loss_fn(output, y) + _, pred = torch.max(output, axis=1) + cur_acc = torch.sum(y == pred) / output.shape[0] + # 反向传播 + optimizer.zero_grad() + cur_loss.backward() + optimizer.step() + loss += cur_loss.item() + current += cur_acc.item() + n = n + 1 + print('train_loss：' + str(loss / n)) + print('train_acc：' + str(current / n)) + +# 定义测试函数 +def test(dataloader, model, loss_fn): + # 将模型转换为验证模式 + model.eval() + loss, current, n = 0.0, 0.0, 0 + # 非训练，推理期用到（测试时模型参数不用更新，所以 no_grad） + with torch.no_grad(): + for batch, (X, y) in enumerate(dataloader): + X, y = X.to(device), y.to(device) + output = model(X) + cur_loss = loss_fn(output, y) + _, pred = torch.max(output, axis=1) + cur_acc = torch.sum(y == pred) / output.shape[0] + loss += cur_loss.item() + current += cur_acc.item() + n = n + 1 + print('test_loss：' + str(loss / n)) + print('test_acc：' + str(current / n)) + +# 开始训练 +epoch = 5 +for t in range(epoch): + lr_scheduler.step() + print(f"Epoch {t + 1}\n----------------------") + train(train_dataloader, model, loss_fn, optimizer) + test(test_dataloader, model, loss_fn) + torch.save(model.state_dict(), "save_model/{}model.pth".format(t)) # 模型保存 +print("Done!") +``` + +（2）使用 Paddle 完成一个图像分类的动态图单机训练例子（MNIST 数据集） + +```python +import paddle +from paddle.vision.transforms import Compose, Normalize +transform = Compose([Normalize(mean=[127.5], + std=[127.5], + data_format='CHW')]) +# 使用 transform 对数据集做归一化 +print('download training data and load training data') +train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) +test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform) +print('load finished') + +import paddle +import paddle.nn.functional as F +class LeNet(paddle.nn.Layer): + def __init__(self): + super(LeNet, self).__init__() + self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2) + self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2) + self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1) + self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2) + self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120) + self.linear2 = paddle.nn.Linear(in_features=120, out_features=84) + self.linear3 = paddle.nn.Linear(in_features=84, out_features=10) + + def forward(self, x): + x = self.conv1(x) + x = F.relu(x) + x = self.max_pool1(x) + x = self.conv2(x) + x = F.relu(x) + x = self.max_pool2(x) + x = paddle.flatten(x, start_axis=1,stop_axis=-1) + x = self.linear1(x) + x = F.relu(x) + x = self.linear2(x) + x = F.relu(x) + x = self.linear3(x) + return x + +#方法一 高层 API +from paddle.metric import Accuracy +model = paddle.Model(LeNet()) # 用 Model 封装模型 +optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) + +# 配置模型 +model.prepare( + optim, + paddle.nn.CrossEntropyLoss(), + Accuracy() + ) + +# 训练模型 +model.fit(train_dataset, + epochs=5, + batch_size=64, + verbose=1 + ) +model.evaluate(test_dataset, batch_size=64, verbose=1) + +#方法 2 基础 API +import paddle.nn.functional as F +train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True) +# 加载训练集 batch_size 设为 64 +def train(model): + model.train() + epochs = 2 + optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) + # 用 Adam 作为优化函数 + for epoch in range(epochs): + for batch_id, data in enumerate(train_loader()): + x_data = data[0] + y_data = data[1] + predicts = model(x_data) + loss = F.cross_entropy(predicts, y_data) + # 计算损失 + acc = paddle.metric.accuracy(predicts, y_data) + loss.backward() + if batch_id % 300 == 0: + print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy())) + optim.step() + optim.clear_grad() +model = LeNet() +train(model) + +test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64) +# 加载测试数据集 +def test(model): + model.eval() + batch_size = 64 + for batch_id, data in enumerate(test_loader()): + x_data = data[0] + y_data = data[1] + predicts = model(x_data) + # 获取预测结果 + loss = F.cross_entropy(predicts, y_data) + acc = paddle.metric.accuracy(predicts, y_data) + if batch_id % 20 == 0: + print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, loss.numpy(), acc.numpy())) +test(model) +``` + +（3）两个程序的运行结果 + +一、PyTorch 程序运行结果 + +```python +#下载数据 +Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz +Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data\MNIST\raw\train-images-idx3-ubyte.gz +9913344it [00:03, 2813467.92it/s] +Extracting ./data\MNIST\raw\train-images-idx3-ubyte.gz to ./data\MNIST\raw + +Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz +Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data\MNIST\raw\train-labels-idx1-ubyte.gz +29696it [00:00, 29740700.00it/s] +Extracting ./data\MNIST\raw\train-labels-idx1-ubyte.gz to ./data\MNIST\raw + +Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz +Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data\MNIST\raw\t10k-images-idx3-ubyte.gz +1649664it [00:01, 1119159.12it/s] +Extracting ./data\MNIST\raw\t10k-images-idx3-ubyte.gz to ./data\MNIST\raw + +Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz +Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data\MNIST\raw\t10k-labels-idx1-ubyte.gz +5120it [00:00, 5302428.76it/s] +Extracting ./data\MNIST\raw\t10k-labels-idx1-ubyte.gz to ./data\MNIST\raw +``` + +```python +#五次 epoch 结果 +Epoch 1 +---------------------- +train_loss：2.301828587023417 +train_acc：0.10976666666666667 +test_loss：2.2998157671610513 +test_acc：0.186 +Epoch 2 +---------------------- +train_loss：2.292567366727193 +train_acc：0.13415 +test_loss：2.268421948369344 +test_acc：0.20193333333333333 +Epoch 3 +---------------------- +train_loss：1.2924396817684174 +train_acc：0.5939 +test_loss：0.5503138323009014 +test_acc：0.82955 +Epoch 4 +---------------------- +train_loss：0.45181470778187116 +train_acc：0.86275 +test_loss：0.3795674962008993 +test_acc：0.88765 +Epoch 5 +---------------------- +train_loss：0.35491258655836183 +train_acc：0.8926666666666667 +test_loss：0.3223567478398482 +test_acc：0.9044666666666666 +``` + +二、Paddle 程序运行结果 + +由于 paddle 文档中提供的数据集下载代码一直报错(已在报错汇总中展示)，故进行了手动下载数据集 + +​ 1、使用高层 API 结果 + +```python +#附第 5 个 epoch +Epoch 5/5 +step 10/938 [..............................] - loss: 0.0325 - acc: 0.9938 - ETA: 35s - 38ms/step +step 20/938 [..............................] - loss: 0.0050 - acc: 0.9922 - ETA: 32s - 35ms/step +step 30/938 [..............................] - loss: 0.0094 - acc: 0.9932 - ETA: 29s - 32ms/step +step 40/938 [>.............................] - loss: 0.0344 - acc: 0.9910 - ETA: 27s - 31ms/step +step 50/938 [>.............................] - loss: 0.0020 - acc: 0.9916 - ETA: 26s - 30ms/step +step 60/938 [>.............................] - loss: 0.0121 - acc: 0.9917 - ETA: 25s - 29ms/step +step 70/938 [=>............................] - loss: 0.0026 - acc: 0.9913 - ETA: 25s - 29ms/step +step 80/938 [=>............................] - loss: 0.0151 - acc: 0.9914 - ETA: 24s - 29ms/step +step 90/938 [=>............................] - loss: 0.0030 - acc: 0.9910 - ETA: 24s - 28ms/step +step 100/938 [==>...........................] - loss: 0.1395 - acc: 0.9905 - ETA: 23s - 28ms/step +step 110/938 [==>...........................] - loss: 0.0344 - acc: 0.9902 - ETA: 23s - 28ms/step +step 120/938 [==>...........................] - loss: 0.0175 - acc: 0.9904 - ETA: 23s - 28ms/step +step 130/938 [===>..........................] - loss: 0.0511 - acc: 0.9899 - ETA: 22s - 28ms/step +step 140/938 [===>..........................] - loss: 0.0136 - acc: 0.9903 - ETA: 22s - 28ms/step +step 150/938 [===>..........................] - loss: 0.0068 - acc: 0.9901 - ETA: 21s - 28ms/step +step 160/938 [====>.........................] - loss: 0.0128 - acc: 0.9898 - ETA: 21s - 28ms/step +step 170/938 [====>.........................] - loss: 0.0447 - acc: 0.9898 - ETA: 21s - 28ms/step +step 180/938 [====>.........................] - loss: 0.0275 - acc: 0.9900 - ETA: 20s - 28ms/step +step 190/938 [=====>........................] - loss: 0.0488 - acc: 0.9901 - ETA: 20s - 28ms/step +step 200/938 [=====>........................] - loss: 0.0593 - acc: 0.9899 - ETA: 20s - 28ms/step +step 210/938 [=====>........................] - loss: 0.0049 - acc: 0.9899 - ETA: 20s - 28ms/step +step 220/938 [======>.......................] - loss: 0.0186 - acc: 0.9898 - ETA: 19s - 27ms/step +step 230/938 [======>.......................] - loss: 0.0214 - acc: 0.9900 - ETA: 19s - 27ms/step +step 240/938 [======>.......................] - loss: 0.0067 - acc: 0.9902 - ETA: 19s - 27ms/step +step 250/938 [======>.......................] - loss: 0.0195 - acc: 0.9902 - ETA: 18s - 27ms/step +step 260/938 [=======>......................] - loss: 0.0310 - acc: 0.9901 - ETA: 18s - 27ms/step +step 270/938 [=======>......................] - loss: 0.0248 - acc: 0.9902 - ETA: 18s - 27ms/step +step 280/938 [=======>......................] - loss: 0.0213 - acc: 0.9901 - ETA: 17s - 27ms/step +step 290/938 [========>.....................] - loss: 0.0156 - acc: 0.9903 - ETA: 17s - 27ms/step +step 300/938 [========>.....................] - loss: 0.0069 - acc: 0.9906 - ETA: 17s - 27ms/step +step 310/938 [========>.....................] - loss: 0.0361 - acc: 0.9904 - ETA: 17s - 27ms/step +step 320/938 [=========>....................] - loss: 0.0418 - acc: 0.9904 - ETA: 16s - 27ms/step +step 330/938 [=========>....................] - loss: 0.0060 - acc: 0.9903 - ETA: 16s - 27ms/step +step 340/938 [=========>....................] - loss: 0.0587 - acc: 0.9903 - ETA: 16s - 27ms/step +step 350/938 [==========>...................] - loss: 0.0434 - acc: 0.9904 - ETA: 16s - 27ms/step +step 360/938 [==========>...................] - loss: 6.9384e-04 - acc: 0.9904 - ETA: 15s - 27ms/step +step 370/938 [==========>...................] - loss: 0.0134 - acc: 0.9904 - ETA: 15s - 27ms/step +step 380/938 [===========>..................] - loss: 0.0278 - acc: 0.9903 - ETA: 15s - 27ms/step +step 390/938 [===========>..................] - loss: 5.5189e-04 - acc: 0.9902 - ETA: 14s - 27ms/step +step 400/938 [===========>..................] - loss: 0.0023 - acc: 0.9904 - ETA: 14s - 27ms/step +step 410/938 [============>.................] - loss: 0.0105 - acc: 0.9904 - ETA: 14s - 27ms/step +step 420/938 [============>.................] - loss: 0.0398 - acc: 0.9901 - ETA: 14s - 27ms/step +step 430/938 [============>.................] - loss: 0.0169 - acc: 0.9902 - ETA: 13s - 27ms/step +step 440/938 [=============>................] - loss: 0.0013 - acc: 0.9902 - ETA: 13s - 27ms/step +step 450/938 [=============>................] - loss: 0.0074 - acc: 0.9901 - ETA: 13s - 27ms/step +step 460/938 [=============>................] - loss: 0.0651 - acc: 0.9899 - ETA: 12s - 27ms/step +step 470/938 [==============>...............] - loss: 0.0130 - acc: 0.9900 - ETA: 12s - 27ms/step +step 480/938 [==============>...............] - loss: 0.0677 - acc: 0.9900 - ETA: 12s - 27ms/step +step 490/938 [==============>...............] - loss: 0.0147 - acc: 0.9901 - ETA: 12s - 27ms/step +step 500/938 [==============>...............] - loss: 0.0120 - acc: 0.9901 - ETA: 11s - 27ms/step +step 510/938 [===============>..............] - loss: 0.0191 - acc: 0.9901 - ETA: 11s - 27ms/step +step 520/938 [===============>..............] - loss: 0.0296 - acc: 0.9901 - ETA: 11s - 27ms/step +step 530/938 [===============>..............] - loss: 0.0488 - acc: 0.9900 - ETA: 11s - 27ms/step +step 540/938 [================>.............] - loss: 0.0239 - acc: 0.9901 - ETA: 10s - 27ms/step +step 550/938 [================>.............] - loss: 0.0303 - acc: 0.9900 - ETA: 10s - 27ms/step +step 560/938 [================>.............] - loss: 0.0287 - acc: 0.9900 - ETA: 10s - 27ms/step +step 570/938 [=================>............] - loss: 0.0375 - acc: 0.9900 - ETA: 9s - 27ms/step +step 580/938 [=================>............] - loss: 0.0197 - acc: 0.9900 - ETA: 9s - 27ms/step +step 590/938 [=================>............] - loss: 0.0265 - acc: 0.9900 - ETA: 9s - 27ms/step +step 600/938 [==================>...........] - loss: 0.0615 - acc: 0.9901 - ETA: 9s - 27ms/step +step 610/938 [==================>...........] - loss: 0.0036 - acc: 0.9901 - ETA: 8s - 27ms/step +step 620/938 [==================>...........] - loss: 0.0079 - acc: 0.9900 - ETA: 8s - 27ms/step +step 630/938 [===================>..........] - loss: 0.0071 - acc: 0.9901 - ETA: 8s - 27ms/step +step 640/938 [===================>..........] - loss: 6.9407e-04 - acc: 0.9902 - ETA: 8s - 27ms/step +step 650/938 [===================>..........] - loss: 0.0024 - acc: 0.9902 - ETA: 7s - 27ms/step +step 660/938 [====================>.........] - loss: 0.0016 - acc: 0.9902 - ETA: 7s - 27ms/step +step 670/938 [====================>.........] - loss: 0.0069 - acc: 0.9901 - ETA: 7s - 27ms/step +step 680/938 [====================>.........] - loss: 0.0023 - acc: 0.9901 - ETA: 6s - 27ms/step +step 690/938 [=====================>........] - loss: 0.0089 - acc: 0.9901 - ETA: 6s - 27ms/step +step 700/938 [=====================>........] - loss: 0.0108 - acc: 0.9900 - ETA: 6s - 27ms/step +step 710/938 [=====================>........] - loss: 0.0155 - acc: 0.9899 - ETA: 6s - 27ms/step +step 720/938 [======================>.......] - loss: 0.0303 - acc: 0.9898 - ETA: 5s - 27ms/step +step 730/938 [======================>.......] - loss: 0.0405 - acc: 0.9898 - ETA: 5s - 27ms/step +step 740/938 [======================>.......] - loss: 0.0304 - acc: 0.9899 - ETA: 5s - 27ms/step +step 750/938 [======================>.......] - loss: 0.0065 - acc: 0.9897 - ETA: 5s - 27ms/step +step 760/938 [=======================>......] - loss: 0.0091 - acc: 0.9898 - ETA: 4s - 27ms/step +step 770/938 [=======================>......] - loss: 0.0371 - acc: 0.9896 - ETA: 4s - 27ms/step +step 780/938 [=======================>......] - loss: 0.0048 - acc: 0.9896 - ETA: 4s - 27ms/step +step 790/938 [========================>.....] - loss: 0.0036 - acc: 0.9897 - ETA: 4s - 27ms/step +step 800/938 [========================>.....] - loss: 0.0233 - acc: 0.9896 - ETA: 3s - 27ms/step +step 810/938 [========================>.....] - loss: 0.0547 - acc: 0.9896 - ETA: 3s - 27ms/step +step 820/938 [=========================>....] - loss: 0.0011 - acc: 0.9896 - ETA: 3s - 27ms/step +step 830/938 [=========================>....] - loss: 0.0079 - acc: 0.9896 - ETA: 2s - 27ms/step +step 840/938 [=========================>....] - loss: 0.0132 - acc: 0.9896 - ETA: 2s - 27ms/step +step 850/938 [==========================>...] - loss: 0.0134 - acc: 0.9896 - ETA: 2s - 27ms/step +step 860/938 [==========================>...] - loss: 0.0065 - acc: 0.9896 - ETA: 2s - 27ms/step +step 870/938 [==========================>...] - loss: 0.0106 - acc: 0.9897 - ETA: 1s - 27ms/step +step 880/938 [===========================>..] - loss: 0.0312 - acc: 0.9896 - ETA: 1s - 27ms/step +step 890/938 [===========================>..] - loss: 0.0169 - acc: 0.9897 - ETA: 1s - 27ms/step +step 900/938 [===========================>..] - loss: 0.0187 - acc: 0.9897 - ETA: 1s - 27ms/step +step 910/938 [============================>.] - loss: 0.0925 - acc: 0.9897 - ETA: 0s - 27ms/step +step 920/938 [============================>.] - loss: 0.0317 - acc: 0.9898 - ETA: 0s - 27ms/step +step 930/938 [============================>.] - loss: 0.0448 - acc: 0.9898 - ETA: 0s - 27ms/step +step 938/938 [==============================] - loss: 0.0140 - acc: 0.9897 - 27ms/step +Eval begin... +step 10/157 [>.............................] - loss: 0.2273 - acc: 0.9828 - ETA: 1s - 12ms/step +step 20/157 [==>...........................] - loss: 0.1525 - acc: 0.9773 - ETA: 1s - 11ms/step +step 30/157 [====>.........................] - loss: 0.1391 - acc: 0.9771 - ETA: 1s - 11ms/step +step 40/157 [======>.......................] - loss: 0.0088 - acc: 0.9785 - ETA: 1s - 11ms/step +step 50/157 [========>.....................] - loss: 0.0051 - acc: 0.9803 - ETA: 1s - 11ms/step +step 60/157 [==========>...................] - loss: 0.1621 - acc: 0.9797 - ETA: 1s - 10ms/step +step 70/157 [============>.................] - loss: 0.0265 - acc: 0.9795 - ETA: 0s - 10ms/step +step 80/157 [==============>...............] - loss: 0.0019 - acc: 0.9801 - ETA: 0s - 10ms/step +step 90/157 [================>.............] - loss: 0.0439 - acc: 0.9814 - ETA: 0s - 10ms/step +step 100/157 [==================>...........] - loss: 0.0033 - acc: 0.9828 - ETA: 0s - 10ms/step +step 110/157 [====================>.........] - loss: 3.9403e-04 - acc: 0.9837 - ETA: 0s - 10ms/step +step 120/157 [=====================>........] - loss: 6.5309e-04 - acc: 0.9846 - ETA: 0s - 10ms/step +step 130/157 [=======================>......] - loss: 0.0735 - acc: 0.9849 - ETA: 0s - 10ms/step +step 140/157 [=========================>....] - loss: 9.8257e-05 - acc: 0.9856 - ETA: 0s - 10ms/step +step 150/157 [===========================>..] - loss: 0.0412 - acc: 0.9859 - ETA: 0s - 10ms/step +step 157/157 [==============================] - loss: 2.9252e-04 - acc: 0.9860 - 10ms/step +Eval samples: 10000 +``` + +​ 2、使用基础 API 结果 + +```python +#附 5 次 epoch +epoch: 0, batch_id: 0, loss is: [2.9994564], acc is: [0.0625] +epoch: 0, batch_id: 300, loss is: [0.08384503], acc is: [0.96875] +epoch: 0, batch_id: 600, loss is: [0.06951822], acc is: [0.984375] +epoch: 0, batch_id: 900, loss is: [0.1054411], acc is: [0.953125] +epoch: 1, batch_id: 0, loss is: [0.0715376], acc is: [0.96875] +epoch: 1, batch_id: 300, loss is: [0.14129372], acc is: [0.953125] +epoch: 1, batch_id: 600, loss is: [0.00361754], acc is: [1.] +epoch: 1, batch_id: 900, loss is: [0.00827341], acc is: [1.] +epoch: 2, batch_id: 0, loss is: [0.05238173], acc is: [0.984375] +epoch: 2, batch_id: 300, loss is: [0.00865405], acc is: [1.] +epoch: 2, batch_id: 600, loss is: [0.03549637], acc is: [0.984375] +epoch: 2, batch_id: 900, loss is: [0.02600437], acc is: [1.] +epoch: 3, batch_id: 0, loss is: [0.02365134], acc is: [1.] +epoch: 3, batch_id: 300, loss is: [0.0848916], acc is: [0.953125] +epoch: 3, batch_id: 600, loss is: [0.01307216], acc is: [1.] +epoch: 3, batch_id: 900, loss is: [0.01843782], acc is: [1.] +epoch: 4, batch_id: 0, loss is: [0.00281677], acc is: [1.] +epoch: 4, batch_id: 300, loss is: [0.01466173], acc is: [1.] +epoch: 4, batch_id: 600, loss is: [0.04725911], acc is: [0.984375] +epoch: 4, batch_id: 900, loss is: [0.00772327], acc is: [1.] +batch_id: 0, loss is: [0.03467739], acc is: [0.984375] +batch_id: 20, loss is: [0.15250863], acc is: [0.953125] +batch_id: 40, loss is: [0.13340972], acc is: [0.984375] +batch_id: 60, loss is: [0.06206714], acc is: [0.953125] +batch_id: 80, loss is: [0.00384411], acc is: [1.] +batch_id: 100, loss is: [0.00386263], acc is: [1.] +batch_id: 120, loss is: [0.00981056], acc is: [1.] +batch_id: 140, loss is: [0.07646853], acc is: [0.984375] + +Process finished with exit code 0 +``` + +这部分简单说就是 Paddle 的高层 API 比基础 API 运行速度快，且简单好用，体验感较好。 + +与 PyTorch 相比，Paddle 文档中提供的代码下载不了数据集，需要手动下载。 + +# 七、各种 trick 的用法 + +PaddlePaddle 有丰富的 api 可以实现各种调参 trick，像 dropout，batchnormalization，groupnormalization，l2regularization, lr decay 等等都可以很轻松地实现。 +另外数据增强则推荐使用 PIL 库，尝试各种技巧不一定每次都能让模型准确度提升，毕竟训练神经网络是一个多参数配合的过程，只有练得多了才更容易找到最佳的方向。 +根据查阅资料，现总结以下几点： +1、 cuDNN 操作的选择 +在 use_cudnn=True 时，框架底层调用的是 cuDNN 中的卷积操作。 +通常 cuDNN 库提供的操作具有很好的性能表现，其性能明显优于 Paddle 原生的 CUDA 实现，比如 conv2d 。 +但是 cuDNN 中有些操作的性能较差，比如： conv2d_transpose 在 batch_size=1 时、pool2d 在 global_pooling=True 时等， +这些情况下，cuDNN 实现的性能差于 Paddle 的 CUDA 实现，建议手动设置 use_cudnn=False 。 + +2、使用融合功能的 API +用户网络配置中使用融合功能的 API，通常能取得更好的计算性能。 +例如 softmax_with_cross_entropy 通常会比 softmax cross_entropy 分开用好 + +3、优化数据准备速度的方法 +为降低训练的整体时间，建议用户使用异步数据读取的方式，并开启 use_double_buffer（默认开）。此外，用户可根据模型的实际情况设置数据队列的大小（capacity）。 +如果数据准备的时间大于模型执行的时间，或者出现了数据队列为空的情况，这时候需要考虑对 Python 的用户 reader 进行加速。常用的方法为：使用 Python 多进程准备数据。 +Python 端的数据预处理，都是使用 CPU 完成。如果 Paddle 提供了相应功能的 API，可将这部分预处理功能写到模型配置中，如此 Paddle 就可以使用 GPU 来完成该预处理功能， +这样也可以减轻 CPU 预处理数据的负担，提升总体训练速度。 + +4、显存优化策略 +GC（Garbage Collection）的原理是在网络运行阶段及时释放无用变量的显存空间，达到节省显存的目的。GC 适用于使用 Executor，ParallelExecutor 做模型训练/预测的场合。 +由于原生的 CUDA 系统调用 cudaMalloc 和 cudaFree 均是同步操作，非常耗时。因此与许多框架类似，PaddlePaddle 采用了显存预分配的策略加速显存分配。 + +5、Inplace 策略 +原理是 Op 的输出复用 Op 输入的显存空间。 +由于目前设计上的一些问题，在开启 Inplace 策略后，必须保证后续 exe.run 中 fetch_list 的变量是 persistable 的。 +fetch_list：结果获取表，训练时一般有 loss 等。 +推荐的最佳显存优化策略为： +开启 Inplace 策略：设置 build_strategy.enable_inplace = True ，并设置 fetch_list 中的 var.persistable = True 。 + +PaddlePaddle 在深度学习框架方面，已经覆盖了搜索、图像识别、语音语义识别理解、情感分析、机器翻译、用户画像推荐等多领域的业务和技术。 +基于动态图实现的 AlexNet 代码如下: + +```python +class ConvPoolLayer(nn.Layer): + '''卷积+池化''' + def __init__(self, + input_channels, + output_channels, + filter_size, + stride, + padding, + stdv, + groups=1, + act=None, + name=None): + super(ConvPoolLayer, self).__init__() + self.relu = ReLU() if act == "relu" else None + + self._conv = Conv2D(#返回一个由所有子层组成的列表。 + in_channels=input_channels, + out_channels=output_channels, + kernel_size=filter_size, + stride=stride, + padding=padding, + groups=groups, + weight_attr=ParamAttr( + name=name + "_weights", initializer=Uniform(-stdv, stdv)), + bias_attr=ParamAttr( + name=name + "_offset", initializer=Uniform(-stdv, stdv))) + self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0) + + def forward(self, inputs): + x = self._conv(inputs) + if self.relu is not None: + x = self.relu(x) + x = self._pool(x) + return x +``` + +PaddlePaddle 搭建 cnn 网络以及进行模型训练预测，可以说 PaddlePaddle 搭建训练 pipeline 还是比较方便的。 + +# 八、报错汇总 + +1、 DataLoader 报错问题 ： + +```python +SystemError: (Fatal) Blocking queue is killed because the data reader raises an exception. +[Hint: Expected killed_ != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158) +``` + +原因分析：由于 PaddlePaddle 和 PyTorch 两个框架在这部分并无区别，Paddle 读取数据在这主要用到两个类：paddle.io.Dataset 和 paddle.io.DataLoader，所以查看源代码后发现在 Dataset 类中的__getitem__(self, idx)返回的数据不是 numpy.ndarray 类型 + +解决方案： + + 在__getitem__（）函数里添加一行代码：image = paddle.to_tensor(image) + +```python +# define a random dataset +class RandomDataset(Dataset): + def __init__(self, num_samples): + self.num_samples = num_samples + + def __getitem__(self, idx): + image = np.random.random([784]).astype('float32') + label = np.random.randint(0, 9, (1, )).astype('int64') + image = paddle.to_tensor(image) # 添加这行代码 + return image, label + + def __len__(self): + return self.num_samples +``` + +注：还有一种情况是 Dataset 类的__getitem__(self, idx)返回的数据为字典（Dict） 类型也会报同样的错误，这时可把 return 改为 return {'input': image, 'lb': label} + + + +2、使用自己数据集的图像大小不合适而报错 + +```python +ERROR:root:DataLoader reader thread raised an exception! +Traceback (most recent call last): +File “/home/disk0/zw/workspace/PaddleOCR/test/load_data.py”, line 38, in +for idx,batch in enumerate(data_loader): +File “/home/disk0/wy/anaconda3/envs/paddle/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py”, line 788, in next +data = self.reader.read_next_var_list() +SystemError: (Fatal) Blocking queue is killed because the data reader raises an exception. +[Hint: Expected killed != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158) +``` + +解决方案：由于自己数据集中有部分图片超过了默认 shape 的[3, 32, 320]，图片宽度大于了 320，所以直接删除或调大 shape 尺寸即可 + +注:使用公开数据集时不会出现此问题 + + + +3、使用 paddle.reshape 时出现错误 + +```python +ValueError: (InvalidArgument) The 'shape' in ReshapeOp is invalid. The input tensor X'size must be equal to the capacity of 'shape'. But received X's shape = [64, 50, 4, 4], X's size = 51200, 'shape' is [1, 800], the capacity of 'shape' is 800. + [Hint: Expected capacity == in_size, but received capacity:800 != in_size:51200.] (at C:\home\workspace\Paddle_release\paddle/fluid/operators/reshape_op.cc:224) + [operator < reshape2 > error] +``` + +解决方案：在使用 forward 函数实现 MNIST 网络的执行逻辑时，忽略了 self.pool_2_shape 变量的大小，重新设置 paddle.reshape 为 x = paddle.reshape(x, shape=[-1, self.pool_2_shape])即可 + + + +4、tensor 的值不能直接利用 + +报错：paddlepaddle 中的 value 不能直接拿出来用。 + +```python +TypeError: The type of 'shape' in reshape must be list[int] or tuple(int) in + Dygraph mode, but received , which contains Variable. +#错误代码：其中 stack_size, feat_size 为 tensor。 + +#改进加入 +# paddlepaddle code +stack_size = stack_size.numpy() +feat_size = feat_size.numpy() +``` + + + +5、Paddle 加载数据集报错，无法下载 MNIST 数据集，需要手动进行下载，（使用了多台电脑测试，均会出现此情况） + +```python +File "E:\anaconda\lib\site-packages\paddle\vision\datasets\mnist.py", line 98, in __init__ + self.image_path = _check_exists_and_download( + File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 207, in _check_exists_and_download + return paddle.dataset.common.download(url, module_name, md5) + File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 82, in download + raise RuntimeError("Cannot download {0} within retry limit {1}". +RuntimeError: Cannot download https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz within retry limit 3 +``` + +```python +File "E:\anaconda\lib\site-packages\paddle\vision\datasets\cifar.py", line 122, in __init__ + self.data_file = _check_exists_and_download( + File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 207, in _check_exists_and_download + return paddle.dataset.common.download(url, module_name, md5) + File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 82, in download + raise RuntimeError("Cannot download {0} within retry limit {1}". +RuntimeError: Cannot download https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz within retry limit 3 +``` + +按照文档提供的'DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10',多种数据集进行了下载测试，均无法在单机上加载数据集，需要手动下载数据集。 + + 且数据集的保存地址为一个缓存空间，用户在使用的时候可能找不到数据集，如/public/home/username/.cache/paddle/dataset 目录。 + + 而 pytorch 的加载数据集 API 会把数据集加载到当前目录，这一点的体验要优于 Paddle。 diff --git a/docs/guides/10_contribution/community_contribution_cn.md b/docs/guides/10_contribution/community_contribution_cn.md new file mode 100644 index 00000000000..dea8119b37b --- /dev/null +++ b/docs/guides/10_contribution/community_contribution_cn.md @@ -0,0 +1,83 @@ +# 飞桨社区贡献指南 + +开源不仅开放源代码，更是一种分享的态度和沟通方式。每一个社区成员都可以通过贡献代码、贡献文档、加入特殊兴趣小组（SIG）、参与讨论与分享等方式，成为飞桨社区的重要一员，与社区开发者们共同进步。 + +## 社区支持 + +### QQ 群/微信群 + +飞桨官方组织了官方交流群，加入社群即可与其他开发者共同交流在深度学习中的所见所得。除此之外，飞桨还有各个垂直方向技术交流群，在这里你将能更快遇到志同道合的开发者，与他们一起交流学习。 +我们鼓励开发者们在社群中发挥所长，发表自己的技术见解，与社群开发者互帮互助，共同成长。 + +[点击加入 PaddlePaddle 开发者技术交流群](https://www.paddlepaddle.org.cn/support/news?action=detail&id=2439) + +## 飞桨论坛 + +作为深度学习爱好者的交流平台，包含原创博客、精品问答、产品交流等内容，为开发者提供纯粹、高质的技术交流平台。 + +[前往论坛](https://aistudio.baidu.com/paddle/forum) + +## 开源协作 + +### GitHub/Gitee + +在这里，开发者可以在开源项目中以 PR 的形式贡献代码，也可以在遇到问题时提交 issue 与相对应的开源贡献者进行交流沟通，飞桨的开源项目都会在这里向大家展示。 +当然，你也可以选择自己喜欢的开源项目，并为它投上一颗宝贵的 Star，让项目开发者感受到来自你的鼓励与支持。 + +GitHub: [PaddlePaddle/Paddle](https://github.com/PaddlePaddle/Paddle)、Gitee：[PaddlePaddle/Paddle](https://gitee.com/paddlepaddle/Paddle) + +### 飞桨特殊兴趣小组(PPSIG) + +由飞桨社区开发者负责和参与的 PPSIG，是飞桨开源社区的重要组成部分。目前有 12 个活跃的 PPSIG，围绕特定领域开展协作。还有更多 PPSIG 正在组建中，欢迎更多开发者们踊跃加入，参与飞桨生态共建。 + +[飞桨特殊兴趣小组(SIG)](https://www.paddlepaddle.org.cn/sig) + +## 技术论坛 + +论坛介绍：作为深度学习爱好者的交流平台，包含原创博客、精品问答、产品交流等内容，为开发者提供纯粹、高质的技术交流平台。 + +[前往论坛](https://aistudio.baidu.com/paddle/forum) + +## 技术布道 + +### 技术文章 + +欢迎向飞桨投稿有技术深度、逻辑流畅、内容清晰文章，我们期待更多来自开发者的优质内容在社区中进行分享，让更多的开发者看到、学习、关注到你和你的项目。入选了飞桨开发者说专栏的文章，将在飞桨公众号、知乎、开源中国 CSDN、飞桨论坛等全平台进行展示和传播。 + +**投稿要求** + +文章基本原则：Why？What？How？ + +- 为什么要做（Why） + - 项目背景主要包括：行业痛点、项目价值 + - 先说明当下遇到的行业痛点，从而引出阐述该项目的价值，包括但不局限于为生活提供便利、促进某个行业发展、趣味十足的小案例等 +- 项目是什么（What） + - 数据集：如用到了数据集，需说明数据集来源，测试集、训练集的比例或数量 + - 模型或算法：如使用了飞桨的模型或算法，请说明飞桨模型的名称和相关 GitHub 链接 + - 项目的效果：文章中最好能有图表来显示效果 +- 怎么做（How） + - 做事情要有始有终，文章也一样需要有一个好的 ending，结尾要包括项目的效果 + +[前往飞桨开发者说文章](https://mp.weixin.qq.com/mp/homepage?__biz=Mzg2OTEzODA5MA==&hid=16&sn=0561fc80d64fc079892454aafeb47bc4&scene=18) + +### 飞桨开发者说 Live 直播 + +通过飞桨开发者说直播的形式，分享在项目开发过程中的实践经验和踩坑破解的方法。我们相信，观看分享的观众也是你的同行者，会带给你无尽的力量。 + +[视频回放](https://space.bilibili.com/476867757/channel/detail?cid=176010) + +### 布道分享 + +以代码和项目为基础，在飞桨官方或开发者自组织的 Meetup/j 技术沙龙中向开发者宣讲技术，让飞桨 PaddlePaddle 提供的各种技术工具，变得更容易被理解和应用，而每一场分享布道的 Meetup，也会带给你更大的行业和技术影响力。 +请联系飞桨小助手（paddle-help）咨询投稿和参与方式。 + + +## 全国各地开源社区 + +飞桨领航团是飞桨开发者的兴趣社区，在各个城市/高校领航团团长及成员的热情支持下，飞桨领航团已建立 150 个社群，覆盖 29 个省级行政区，133 个高校，并且在持续增长中。开发者们可以参与丰富的本地技术沙龙、Meetup、及线上交流。 + +飞桨领航团面向所有对人工智能及深度学习领域感兴趣的开发者开放，欢迎广大开发者们加入领航团，结识更多本地技术同好，共享开源成果与快乐。 + + +[查看我所在的本地开源社区](https://www.paddlepaddle.org.cn/ppdenavigategroup) +加入飞桨领航团 QQ 群：484908840 diff --git a/docs/guides/10_contribution/faq_cn.rst b/docs/guides/10_contribution/faq_cn.rst new file mode 100644 index 00000000000..6d1a7590991 --- /dev/null +++ b/docs/guides/10_contribution/faq_cn.rst @@ -0,0 +1,42 @@ +.. _contribute_to_paddle_faq: + +################### +FAQ +################### + +.. contents:: + +1. CLA 签署不成功，怎么办？ +--------------------------- + +由于 `CLA `_ 是第三方开源库，有时候会不稳定。如果确定自己已签署 CLA，但 CLA 没触发成功，可尝试： + +* 关闭并重新开启本 PR，来重新触发 CLA。点击 :code:`Close pull request` ，再点击 :code:`Reopen pull request` ，并等待几分钟。 +* 如果上述操作重复 2 次仍未生效，请重新提一个 PR 或评论区留言。 + +2. CI 没有触发，怎么办？ +------------------------ + +* 请在 commit 信息中添加正确的 CI 触发规则： + + * develop 分支请添加 :code:`test=develop` + * release 分支请添加如 :code:`test=release/1.4` 来触发 release/1.4 分支 + * 文档预览请添加 :code:`test=document_preview` + +* 该 CI 触发规则以 commit 为单位，即对同一个 PR 来说，不管前面的 commit 是否已经添加，如果新 commit 想继续触发 CI，那么仍然需要添加。 +* 添加 CI 触发规则后，仍有部分 CI 没有触发：请关闭并重新开启本 PR，来重新触发 CI。 + + +3. CI 随机挂，即错误信息与本 PR 无关，怎么办？ +-------------------------------------- + +由于 develop 分支代码的不稳定性，CI 可能会随机挂。 +如果确定 CI 错误和本 PR 无关，请在评论区贴上错误截图和错误链接。 + +4. 如何修改 API.spec？ +----------------------- + +为了保证 API 接口/文档的稳定性，我们对 API 进行了监控，即 API.spec 文件。 +修改方法请参考 `diff_api.py `_ 。 + +**注意**：提交 PR 后请查看下 diff，不要改到非本 PR 修改的 API 上。 diff --git a/docs/guides/10_contribution/hackathon_cn.md b/docs/guides/10_contribution/hackathon_cn.md new file mode 100644 index 00000000000..a5e0e3316dd --- /dev/null +++ b/docs/guides/10_contribution/hackathon_cn.md @@ -0,0 +1,338 @@ +## 一、活动介绍 + +第六期 PaddlePaddle Hackathon 飞桨黑客马拉松即将上线，本次活动是面向全球开发者的深度学习领域编程活动，鼓励开发者了解与参与飞桨深度学习开源项目的开发实践。 + +本次飞桨黑客马拉松由深度学习技术及应用国家工程研究中心主办，百度飞桨承办，活动包含以下四大赛道，等你来挑战！ + +- **开源贡献个人挑战赛** +- **飞桨护航计划集训营** +- **Fundable Projects** +- **优秀稿件征集与传播** + +对深度学习感兴趣的你，快来参加活动吧，更有机会获得现金奖励以及来自百度和开源社区技术大咖们的指导与经验分享，更多奖励等你来拿。 + +> 1. [第六期黑客松活动官网落地页](https://www.paddlepaddle.org.cn/PaddlePaddleHackathon-2024-3) +> 2. [第六期黑客松任务总览](https://github.com/PaddlePaddle/Paddle/issues/62904) + +### 1. 开源贡献个人挑战赛 + +贡献任务详见 issue： **https://github.com/PaddlePaddle/Paddle/issues/62905** + +以开源贡献为核心，发布 **5+** 开源项目的 **50+** 贡献任务，开源项目包括：飞桨核心框架、飞桨科学计算、生态伙伴项目等。**开发者使用 GitHub ID 报名，独立完成具有明确验收目标的贡献任务，获得对应的任务奖励。** + +#### 1.1 活动流程 + +**(1) 报名** + +在上述任务 issue 下**按格式**回复报名信息。 + +**报名格式参考：**`【报名】：2、3`，多个任务之间需要使用中文顿号分隔，报名多个连续任务可用横线表示，如 `2-5` + +**(2) 提交 RFC（可选）** + +请根据任务要求提交 RFC（Request for Comments）到 [PaddlePaddle/community/rfcs](https://github.com/PaddlePaddle/community/tree/master/rfcs) 目录，与飞桨团队沟通任务细节与后续开发规划。 + +> [!NOTE] +> 如果任务标注不需要 RFC，可跳过这一步直接进行代码开发。 + +**(3) 完成设计文档（可选）** + +任务要求提交 RFC 的，必须等 RFC 通过后再进行开发。RFC 合入后，任务榜单会更新任务状态为**完成设计文档**，后续开发需遵循 RFC 设计，否则提交会被拒绝（PR close）。 + +**(4) 提交 PR** + +开发完成后，提交 PR（Pull Requests）到任务指定的代码仓库，请确保你的 PR 包含清晰的代码结构和文档，以便审查（PR review）和合并（PR merge）。 + +> [!NOTE] +> **PR 提交格式要求：在 PR 的标题中以 【Hackathon 6th No.xxx】 开头，注明任务编号** + +**(5) 完成任务** + +在任务提交后 3 个工作日内，黑客松评审组将对 PR 进行验收并给出相应反馈：如果该提交符合要求，PR 顺利通过验收评审、 CI 测试并被合并（PR merge），视为任务完成，**该任务后续不再验收其他提交**。 + +**(6) 奖金发放** + +本期黑客松赛程全部结束后，活动组委会统一进行成果公示与奖金发放，你将根据任务难度获得相应的奖励，难度在任务发布时以 🌟 的数量标识，**一颗 🌟 对应￥ 2,000 奖金。** + +#### 1.2 活动日程 + +| **时间** | **日程** | +| :------------------------: | :------------------------: | +| **2024/03/20 19:00 GMT+8** | 活动正式上线，发布贡献任务 | +| **2024/05/29 19:00 GMT+8** | RFC 提交截止 | +| **2024/06/12 19:00 GMT+8** | PR 提交截止 | +| **2024/06/17 19:00 GMT+8** | 结果公示 | + +### 2. 飞桨护航计划集训营 + +> [!IMPORTANT] +> 护航计划集训营已于 **2024/01/22** 开放报名，**第二批补招**将于 **2024/03/20** 开放报名。 + +集训项目详见 issue： +- 第一批 https://github.com/PaddlePaddle/Paddle/issues/61006 +- 第二批 https://github.com/PaddlePaddle/Paddle/issues/62906 + +#### 2.1 活动流程 + +**(1) 报名** + +请在上述 [issue](https://github.com/PaddlePaddle/Paddle/issues/61006) 下**按格式**回复报名信息。 + +**报名格式参考：** + +``` +【报名】：1 +【其他意向】：2、4、6 +不接受调剂 +``` + +请注意报名序号填写第一意向，如有其他意向或接收调剂，可备注在报名邮件和 issue 评论区中。 + +报名完成后，请依据集训项目中的营员要求撰写简历，邮件发送至 [ext_paddle_oss@baidu.com](mailto:paddle-hack@baidu.com) 等候通知。 + +**(2) 导师面试** + +依据项目报名情况，导师进行简历筛选和面试，请及时关注预留的联系方式，**营员选拔信息每周更新到报名 issue 中。导师锁定营员后，营员需在 3 个工作日内答复是否参与该集训项目，需保证参与连续 3 个月的集训，并完成预期研发目标。营员确认后次日开启集训，由导师安排具体工作。** + +**(3) 集训期** + +营员以远程的方式深度参与开源项目开发实践，成果以 PR（Pull Requests）的形式贡献到指定代码仓库。导师指导营员按期完成研发目标，每 2 周更新一次项目报告，接收黑客松组委会监督。 + +**(4) 营员考核** + +黑客松组委会将于 **2024/06/03 - 2024/06/07 期间** 组织营员集中答辩，考核集训项目完成度与研发质量，**只有通过考核的营员才能获得对应的活动奖金与证书。** + +> [!IMPORTANT] +> 未满 3 个月集训时间的营员（如补招的营员），也将在 **2024/06/03 - 2024/06/07 期间** 提前参加答辩。但仍需继续进行集训开发，直至集训期满 3 个月。 + +**(5) 结营评估** + +依据营员开启集训的日期，在完成 3 个月集训后进行结营评估，评估结果分为 3 档，对应不同星级： + +- 及格：🌟🌟🌟 +- 良好：🌟🌟🌟🌟 +- 优秀：🌟🌟🌟🌟🌟 + +**(6) 奖金发放** + +本期护航计划集训营全部结束后，黑客松组委会统一进行成果公示与奖金发放，你将根据结营评估结果获得相应的奖励，**一颗 🌟 对应￥ 2,000 奖金。** + +#### 2.2 活动日程 + +| **时间** | **日程** | +| :-------------------------: | :------------------------: | +| **2024/01/22 18:00 GMT+8** | 发布集训项目，启动营员招募 | +| **2024/01/22 - 2023/02/23** | 公开接收简历，安排面试 | +| **2024/02/26 19:00 GMT+8** | 营员招募截止，公布招募名单 | +| **2024/02/29 16:00 GMT+8** | 开营仪式，集训营正式启动 | +| **2024/03/20 19:00 GMT+8** | 第二批补招开放 | +| **2023/06/03-2023/06/07** | 营员集中答辩 | +| **2023/06/12 23:59 GMT+8** | 集训营结营，公布考核结果 | + + +### 3. Fundable Projects + +贡献任务详见 issue： **https://github.com/PaddlePaddle/Paddle/issues/62908** + +定位**硬核项目**，要求开发者能够**独立拆解任务需求、梳理开发路径，并且号召社区共同实现某一功能需求**，最终以功能完成度为标准发放奖金。 + +#### 3.1 活动流程 + +**(1) 报名** + +在任务 issue 下**按格式**回复报名信息。 + +**报名格式参考：** + +``` +【报名】：1 +``` + +报名完成后，请提交 **简历** 或 **任务技术说明**，邮件发送至 ext_paddle_oss@baidu.com 等候通知。 + +> [!NOTE] +> 由于该赛道任务难度较大，同一任务允许有多名开发者报名，报名后将进行协商组队（基于报名者意愿的前提下）。 + +**(2) 筛选与沟通** + +依据报名情况、提交的简历或任务技术说明进行**筛选**，确保开发者能力与任务难度匹配。 + +报名通过后，会邀请入选成员进群，与飞桨研发进行技术沟通，最终敲定 RFC，并与队友确定分工（如有）。 + +**(3) 提交 RFC** + +根据沟通细节和项目技术梳理，主动进行任务拆解并提交 RFC（Request for Comments）到 [PaddlePaddle/community/rfcs](https://github.com/PaddlePaddle/community/tree/master/rfcs) 目录，参考 [RFC 设计文档模板](https://github.com/PaddlePaddle/community/blob/master/rfcs/design_template.md)，对任务进行技术方案设计，并与飞桨社区维护者共同讨论任务细节与分工。 + +**(4) 完成设计文档、发布 Tracking Issue** + +RFC 通过并合入后形成 Tracking Issue。原则上完成 RFC 的开发者会成为该任务的 leader，带领队员协作完成任务（任务 leader 优先获得参与决赛答辩的机会）。 + +> [!NOTE] +> +> 1. 优秀 RFC 案例：[将 xdoctest 引入到飞桨框架工作流中](https://github.com/PaddlePaddle/community/blob/master/rfcs/Docs/将%20xdoctest%20引入到飞桨框架工作流中.md) +> 2. 优秀 Tracking Issue 案例：[将 xdoctest 引入到飞桨框架工作流中 Tracking Issue](https://github.com/PaddlePaddle/Paddle/issues/54705) + +**(6) 任务开发、提交作品** + +根据任务描述进行开发，如有需要发动社区开发者协助的批量任务，可在群组联系相关负责人进行进一步沟通。 + +确保开发内容完整后，以 Pull Requests（PR）形式提交至指定的代码仓库，待 CI 大部分通过后，启动技术评审，技术评审启动后，你的 PR 会由任务导师 & 队员进行技术层面的评审与交流，你需要时刻关注反馈并与任务导师沟通作品细节，如果修改完毕，需要在 PR 下回复「修改完毕。 + +**(7) 作品验收与答辩** + +在通过技术评审后，你会获得技术 approve，PR 被 merge 表示你完成了一次开源贡献，可继续选择其他未完成的子任务进行开发。 + +在 **06/13-06/14** 期间，各团队需要准备材料参与答辩，飞桨研发将根据答辩情况和作品完成度进行最终评审。 + +**(8) 奖金发放** + +本期黑客松赛程全部结束后，活动组委会统一进行成果公示与奖金发放，你将根据任务难度获得相应的奖励，难度在任务发布时以 🌟 的数量标识，**一颗 🌟 对应￥ 2,000 奖金。** + +> [!IMPORTANT] +> 本赛道的各任务，项目完成度在 **80%** 以上可获得奖金，并按项目完成度比例发放。 + +#### 3.2 产出要求 + +1. 任务拆解 tracking issue +2. 答辩 PPT +3. 书面的技术报告 +4. 代码运行无误，通过社区 maintainers 的评审并合入代码仓库。 + + +#### 3.3 活动日程 + +| **时间** | **日程** | +| :------------------------: | :------------------------: | +| **2024/03/20 19:00 GMT+8** | 活动正式上线，发布贡献任务 | +| **2024/05/29 19:00 GMT+8** | RFC 提交截止 | +| **2024/06/12 19:00 GMT+8** | PR 提交截止 | +| **2023/06/13-2023/06/14** | 集中答辩 | +| **2024/06/17 19:00 GMT+8** | 结果公示 | + +### 4. 优秀稿件征集与传播 + +贡献任务详见 issue： **https://github.com/PaddlePaddle/Paddle/issues/62907** + +随着版本的更新，飞桨功能发生了非常大的变化。为了让更多的开发者了解这些信息，帮助飞桨传播正面影响力。本期黑客松新增**优秀稿件征集与传播**赛道，开发者可以撰写【选定知识点内】关于飞桨的任何文章，并通过任意形式进行影响力的传播。 + +#### 4.1 活动形式（两阶段） + +**阶段 1：撰写命题制的科技稿件** + +选手可以写 **【选定知识点内】** 的飞桨学习心得、如何使用飞桨等任意有助于扩大飞桨影响力的文章，内含飞桨知识点数量无限制。需要提 PR 到 [community/rfcs/Article)](https://github.com/PaddlePaddle/community/tree/master/rfcs/Article)，经研发审核合入后，可拿到阶段 1 的奖金。请注意： + +- **截止时间：** 截止 **5 月 15 日**，研发不再审核阶段 1 的稿件 PR； +- **奖金名额：** 不限制认领人数，一个题目可以有多篇稿件合入。共 20 个奖金名额，每个名额 0.5⭐️，先合入先得； + +> [!WARNING] +> 如果奖金名额用完但阶段 1 的截止时间没到，选手 A 可以继续进行阶段 1 的稿件创作，研发审核通过后可参加阶段 2 的影响力传播评选。即**选手 A 拿不到阶段 1 的奖金，但有可能拿到阶段 2 的奖品。** + +**阶段 2：影响力传播** + +当稿件成功审核通过后，选手需要以合入后的科技稿件为素材，进行影响力传播。可直接发文或制作成短视频等，形式不限。 + +- **传播渠道：** 选手可以选择自己熟悉的阵地，如微信公众号、知乎、B 站、小红书、YouTube、抖音等等；也可选择飞桨的[开源社区博客](https://pfcc.blog/)、[AIStudio 项目](https://aistudio.baidu.com/projectoverview/public)上进行传播，传播渠道范围和数量均不限。 +- **奖励评定：** 传播时间至少半个月，选手需提供后台数据（如小红书浏览量/赞/藏/评/粉丝）。结合后台数据和实际传播效果，最终评选 1/2/3 等奖，拿到阶段 2 的奖品（京东卡 1000/600/400）。 + +#### 4.2 活动流程 + +**(1) 报名** + +在上述任务 issue 下**按格式**回复报名信息。 + +**报名格式参考：**`【报名】：2、3`，多个任务之间需要使用中文顿号分隔，报名多个连续任务可用横线表示，如 `2-5` + +**(2) 提交 PR** + +稿件撰写完成后，以提交 PR（Pull Requests）到任务指定的代码仓库，请确保你的 PR 包含清晰、规范的文档结构和代码实例，以便审查（PR review）和合并（PR merge）。 + +> [!NOTE] +> **PR 提交格式要求：在 PR 的标题中以 【Hackathon 6th Article No.xxx】 开头，注明任务编号** + +**(3) 稿件审核通过** + +经研发审核无误后，你的稿件 PR 会被合入，并获得 1000 元（0.5🌟）奖励名额，**先合入先得**。当奖金名额占满后，仍能继续提交稿件，此时无法获得稿件奖励（阶段一），但可以参加后续的影响力传播评选。 + +**(4) 影响力传播** + +以合入的稿件为素材，选手进行影响力传播。传播时间至少半个月，传播形式和传播渠道均不限，最终选手需要提供后台数据（如小红书浏览量/赞/藏/评/粉丝）参与影响力传播的评选。组委会结合数据和实际传播效果，最终评选**一、二、三等奖**。 + +**(5) 奖金发放** + +本期黑客松赛程全部结束后，活动组委会统一进行成果公示与奖金发放，稿件奖励统一以半颗 🌟 的数量标识，**半颗 🌟 对应￥ 1,000 奖金。** + +影响力传播的奖励以京东卡的形式发放，一、二、三等奖分别对应 1000、600、400 元京东卡。 + +#### 4.3 活动日程 + +| **时间** | **日程** | +| :------------------------: | :--------------------: | +| **2024/03/20 19:00 GMT+8** | 活动正式上线，发布选题 | +| **2024/05/15 19:00 GMT+8** | 稿件提交截止 | +| **2024/06/12 19:00 GMT+8** | 影响力传播截止 | +| **2024/06/17 19:00 GMT+8** | 结果公示 | + +### **特别注意：** + +> [!WARNING] +> +> 1. **以上所有提及金额均为税前金额。** +> 2. **任务奖项评定需开发者提供完整作品（包括任务对应提交内容以及活动总结等）。** + +## 二、参与方式 + +### 1. 活动参与对象 + +本次活动面向全社会开放，不限年龄、身份、国籍，相关领域的个人、高校、科研机构、企业单位、初创团队等人员均可报名参赛。 + +#### 特别注意： + +（1）活动主办、合作项目单位，以及有机会接触任务背景的员工不得参与本次活动； + +（2）百度公司员工可参与比赛，但不得领取奖金。 + +### 2. 活动须知 + +（1）支持以个人或团队的形式报名并认领任务，允许跨单位自由组队； + +（2）报名须保证所提供的个人信息真实、准确、有效。如发放奖金或礼品时发现报名人员填写的报名信息与个人身份不相符，组委会将保留停止发放奖金或礼品的权利。 + +### 3. 活动报名 + +（1）活动期间均可报名，可在 **[活动页面](https://www.paddlepaddle.org.cn/PaddlePaddleHackathon-2024-3)** 查看具体活动信息； + +（2）请在赛道对应的 issue 下**按格式**回复报名信息，完成任务认领。 + +**补充信息**： + +（1）四大赛道开发任务不冲突，不限制报名次数，开发者可自由选择感兴趣的开发任务进行报名和参与； + +（2）**官方交流群：请扫码进第六期黑客松官方群~** + +加群二维码 + +### 4. 任务开发 + +当你完成相应开发后，相应的代码需要以 Pull Request 的形式上传到指定的代码仓库，黑客松小助手会自动更新你的进度到任务表单。黑客松评审组一般会在 3 个工作日内给出作品反馈，如果符合任务要求，则实时通报该任务已经被完成。 + +**注意**： + +（1）**任务进度由黑客松小助手每 2h 更新到 issue 榜单中；** + +（2）如果发现有恶意提交或抄袭等不正当行为，将会取消参与活动资格； + +（3）活动奖金与开发者获得的 🌟 数量挂钩，奖金发放上限￥ 30,000，**超出奖金上限将按照￥ 30,000 进行发放，但不限制开发者获取更多 🌟；** + +**（4）获取 🌟 数排名前 3 的开发者当选飞桨开源之星。** + +## 三、反作弊声明 + +- 参与者禁止注册多账户报名，一经发现取消获奖资格； +- 参与者禁止在指定考核技术能力的范围外利用规则漏洞或技术漏洞等不良途径参与活动，一经发现取消获奖资格； +- 可以接触到任务的相关人员，其提交作品不计入奖励范围。 + +## 四、其他 + +- 飞桨黑客松不收取任何报名费用； +- 请遵守飞桨开源社区 [Code-of-Conduct](https://github.com/PaddlePaddle/Paddle/blob/develop/CODE_OF_CONDUCT_cn.md)； +- 百度在法律法规许可范围内对本活动规则享有解释权。 diff --git a/docs/guides/10_contribution/img/change_base.png b/docs/guides/10_contribution/img/change_base.png new file mode 100644 index 0000000000000000000000000000000000000000..7e08b96ec882c37132ff1ce3ec3044234eb84c7c GIT binary patch literal 26491 zcmagE1ymi)mNtw8O&~~chv4pR!GpU6cR9Gj!6kTbcXxMpcXxLU?))cj?%X?T{ol-- zwfb~*ch%ne*;ZA%J6KLe3;`Y+9t;c&K|)+u0SpXk71UONg$Di4ktqSez&@dv2?@zb z2ni9&*;yN#SsH`R$? 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|飞桨动静转换评估 | -| --- | --- | -|提交作者 | 王源 袁闯闯 | -|提交时间 | 2022-05-04 | -|版本号 | V1.0 | -|依赖飞桨版本 | V2.2 | -|文件名 | 【Hackathon No.111】PR.md | - -一个完整的使用动静转换@to_static 导出、可部署的模型完整代码（参考以图搜图） - -以下为 AI Studio 任务链接 (cifar10 动态图转为静态图（完整 demo）) - -AI Studio 任务链接:https://aistudio.baidu.com/aistudio/projectdetail/3937674?channel=0&channelType=0&shared=1 - -# 1、任务描述： - - 飞桨框架于 2.0 正式版之后正式发布了动静转换@to_static 功能，并在 2.1、2.2 两个大版本中不断新增了各项功能，以及详细的使用文档和最佳实践教程（以图搜图）。 - - 在本任务中，我们希望你全面体验飞桨的动静转换@to_static 功能，即参考飞桨官网 -> 使用指南 -> 动态图转静态 下的内容，体验动转静模型导出、动转静训练等功能， - - 产出一份整体功能体验报告。 - -# 2、环境配置： - - 因为需要体验飞桨 paddlepaddle 框架的动转静模型导出、动转静训练等功能，所以首先需要安装飞桨 paddlepaddle 框架，运行环境使用 pycharm 和 anaconda。 - - 所以在进行 PaddlePaddle 安装之前应确保 Anaconda 软件环境已经正确安装。软件下载和安装参见 Anaconda 官网(https://www.anaconda.com/)。 - - 在已经正确安装 Anaconda 的情况下请按照下列步骤安装 PaddlePaddle。 - - -Windows 7/8/10 专业版/企业版 (64bit) - - -GPU 版本支持 CUDA 10.1/10.2/11.2，且仅支持单卡 - - -conda 版本 4.8.3+ (64 bit) - -## 2.1、创建虚拟环境： - -- 1、安装环境 - 首先根据具体的 Python 版本创建 Anaconda 虚拟环境，PaddlePaddle 的 Anaconda 安装支持以下四种 Python 安装环境。 -如果您想使用的 python 版本为 3.6: -```python -conda create -n paddle_env python=3.6 -``` -如果您想使用的 python 版本为 3.7: -```python -conda create -n paddle_env python=3.7 -``` -如果您想使用的 python 版本为 3.8: -```python -conda create -n paddle_env python=3.8 -``` -如果您想使用的 python 版本为 3.9: -```python -conda create -n paddle_env python=3.9 -``` -本实验使用 python 版本为 3.9,虚拟环境命名为 paddlepaddle-gpu，即： -```python -conda create -n paddlepaddle-gpu python=3.9 -``` -- 2、进入 Anaconda 虚拟环境 -```python -activate paddlepaddle-gpu -``` -- 3、开始安装 -GPU 版的 PaddlePaddle -本实验为 CUDA 10.2，需要搭配 cuDNN 7 (cuDNN>=7.6.5, 多卡环境下 NCCL>=2.7) -添加清华源（可选），对于国内用户无法连接到 Anaconda 官方源的可以按照以下命令添加清华源: -```python -conda install paddlepaddle-gpu==2.2.2 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -``` -您可参考 NVIDIA 官方文档了解 CUDA 和 CUDNN 的安装流程和配置方法。 -- 4、验证安装 -安装完成后您可以使用 python 或 python3 进入 python 解释器，输入 import paddle ，再输入 paddle.utils.run_check() - -如果出现 PaddlePaddle is installed successfully!，说明您已成功安装。 - -## 2.2、paddlepaddle 环境配置： -```python -import paddle -import paddle.nn.functional as F -import numpy as np -import random -import matplotlib.pyplot as plt -from PIL import Image -from collections import defaultdict - -print(paddle.__version__) -``` -```python -2.2.2 -``` - -# 3、数据加载： - -## 3.1、数据集介绍 - -本示例采用 CIFAR-10 数据集。 - -CIFAR-10 和 CIFAR-100 是 8000 万个微小图像数据集的标记子集。它们由 Alex Krizhevsky、Vinod Nair 和 Geoffrey Hinton 收集。 - -数据集分为五个训练批次和一个测试批次，每个批次有 10000 张图像。测试批次恰好包含来自每个类别的 1000 个随机选择的图像。训练批次包含随机顺序的剩余图像， - -但一些训练批次可能包含来自一个类的图像多于另一个。在它们之间，训练批次恰好包含来自每个类别的 5000 张图像。 - -以下是数据集中的类： - - - 飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船、卡车 - - -这些类是完全互斥的。汽车和卡车之间没有重叠。“汽车”包括轿车、SUV 之类的东西。“卡车”只包括大卡车。两者都不包括皮卡车。 - -数据集下载网址为：https://www.cs.toronto.edu/~kriz/cifar.html -```python -import paddle.vision.transforms as T - -transform = T.Compose([T.Transpose((2, 0, 1))]) - -cifar10_train = paddle.vision.datasets.Cifar10(mode='train', transform=transform) -x_train = np.zeros((50000, 3, 32, 32)) -y_train = np.zeros((50000, 1), dtype='int32') - -for i in range(len(cifar10_train)): - train_image, train_label = cifar10_train[i] - - # normalize the data - x_train[i,:, :, :] = train_image / 255. - y_train[i, 0] = train_label - -y_train = np.squeeze(y_train) - -cifar10_test = paddle.vision.datasets.cifar.Cifar10(mode='test', transform=transform) -x_test = np.zeros((10000, 3, 32, 32), dtype='float32') -y_test = np.zeros((10000, 1), dtype='int64') - -for i in range(len(cifar10_test)): - test_image, test_label = cifar10_test[i] - - # normalize the data - x_test[i,:, :, :] = test_image / 255. - y_test[i, 0] = test_label - -y_test = np.squeeze(y_test) - -height_width = 32 - -def show_collage(examples): - box_size = height_width + 2 - num_rows, num_cols = examples.shape[:2] - - collage = Image.new( - mode="RGB", - size=(num_cols * box_size, num_rows * box_size), - color=(255, 255, 255), - ) - for row_idx in range(num_rows): - for col_idx in range(num_cols): - array = (np.array(examples[row_idx, col_idx]) * 255).astype(np.uint8) - array = array.transpose(1,2,0) - collage.paste( - Image.fromarray(array), (col_idx * box_size, row_idx * box_size) - ) - - collage = collage.resize((2 * num_cols * box_size, 2 * num_rows * box_size)) - return collage - -sample_idxs = np.random.randint(0, 50000, size=(5, 5)) -examples = x_train[sample_idxs] -show_collage(examples) -``` -## 3.2、构建训练数据 - -图片检索的模型的训练样本跟常见的分类任务的训练样本不太一样的地方在于，每个训练样本并不是一个(image, class)这样的形式。而是（image0, image1, similary_or_not)的形式，即，每 - -一个训练样本由两张图片组成，而其 label 是这两张图片是否相似的标志位（0 或者 1）。 - -很自然的能够想到，来自同一个类别的两张图片，是相似的图片，而来自不同类别的两张图片，应该是不相似的图片。 - -为了能够方便的抽样出相似图片（以及不相似图片）的样本，先建立能够根据类别找到该类别下所有图片的索引。 -```python -class_idx_to_train_idxs = defaultdict(list) -for y_train_idx, y in enumerate(y_train): - class_idx_to_train_idxs[y].append(y_train_idx) - -class_idx_to_test_idxs = defaultdict(list) -for y_test_idx, y in enumerate(y_test): - class_idx_to_test_idxs[y].append(y_test_idx) - -num_classes = 10 - -def reader_creator(num_batchs): - def reader(): - iter_step = 0 - while True: - if iter_step >= num_batchs: - break - iter_step += 1 - x = np.empty((2, num_classes, 3, height_width, height_width), dtype=np.float32) - for class_idx in range(num_classes): - examples_for_class = class_idx_to_train_idxs[class_idx] - anchor_idx = random.choice(examples_for_class) - positive_idx = random.choice(examples_for_class) - while positive_idx == anchor_idx: - positive_idx = random.choice(examples_for_class) - x[0, class_idx] = x_train[anchor_idx] - x[1, class_idx] = x_train[positive_idx] - yield x - - return reader - -def anchor_positive_pairs(num_batchs=100): - return reader_creator(num_batchs) - -pairs_train_reader = anchor_positive_pairs(num_batchs=1000) -``` -# 4、模型组网： -把图片转换为高维的向量表示的网络 -目标是首先把图片转换为高维空间的表示，然后计算图片在高维空间表示时的相似度。 下面的网络结构用来把一个形状为(3, 32, 32)的图片转换成形状为(8,)的向量。在有些资料中也会把这个转换成的向量称为 Embedding，请注意，这与自然语言处理领域的词向量的区别。 下面的模型由三个连续的卷积加一个全局均值池化，然后用一个线性全链接层映射到维数为 8 的向量空间。为了后续计算余弦相似度时的便利，还在最后做了归一化。（即，余弦相似度的分母部分） -```python -class MyNet(paddle.nn.Layer): - def __init__(self): - super(MyNet, self).__init__() - - self.conv1 = paddle.nn.Conv2D(in_channels=3, - out_channels=32, - kernel_size=(3, 3), - stride=2) - - self.conv2 = paddle.nn.Conv2D(in_channels=32, - out_channels=64, - kernel_size=(3,3), - stride=2) - - self.conv3 = paddle.nn.Conv2D(in_channels=64, - out_channels=128, - kernel_size=(3,3), - stride=2) - - self.gloabl_pool = paddle.nn.AdaptiveAvgPool2D((1,1)) - - self.fc1 = paddle.nn.Linear(in_features=128, out_features=8) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(x) - x = self.conv2(x) - x = F.relu(x) - x = self.conv3(x) - x = F.relu(x) - x = self.gloabl_pool(x) - x = paddle.squeeze(x, axis=[2, 3]) - x = self.fc1(x) - x = x / paddle.norm(x, axis=1, keepdim=True) - return x -``` -# 5、模型训练： - -将 epoch 设置为 10，进行模型训练 - -```python -def train(model): - print('start training ... ') - model.train() - - inverse_temperature = paddle.to_tensor(np.array([1.0/0.2], dtype='float32')) - - epoch_num = 10 - - opt = paddle.optimizer.Adam(learning_rate=0.0001, - parameters=model.parameters()) - - for epoch in range(epoch_num): - for batch_id, data in enumerate(pairs_train_reader()): - anchors_data, positives_data = data[0], data[1] - - anchors = paddle.to_tensor(anchors_data) - positives = paddle.to_tensor(positives_data) - - anchor_embeddings = model(anchors) - positive_embeddings = model(positives) - - similarities = paddle.matmul(anchor_embeddings, positive_embeddings, transpose_y=True) - similarities = paddle.multiply(similarities, inverse_temperature) - - sparse_labels = paddle.arange(0, num_classes, dtype='int64') - - loss = F.cross_entropy(similarities, sparse_labels) - - if batch_id % 500 == 0: - print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, loss.numpy())) - loss.backward() - opt.step() - opt.clear_grad() - -model = MyNet() -train(model) -``` - -```python -start training ... -epoch: 0, batch_id: 0, loss is: [2.2915785] -epoch: 0, batch_id: 500, loss is: [2.4421422] -epoch: 0, batch_id: 1000, loss is: [1.7860969] -epoch: 0, batch_id: 1500, loss is: [2.3819976] -epoch: 1, batch_id: 0, loss is: [2.2505655] -epoch: 1, batch_id: 500, loss is: [1.6781666] -epoch: 1, batch_id: 1000, loss is: [1.8037045] -epoch: 1, batch_id: 1500, loss is: [1.8967863] -epoch: 2, batch_id: 0, loss is: [1.775666] -epoch: 2, batch_id: 500, loss is: [2.0136874] -epoch: 2, batch_id: 1000, loss is: [2.103913] -epoch: 2, batch_id: 1500, loss is: [1.7592652] -epoch: 3, batch_id: 0, loss is: [1.7325624] -epoch: 3, batch_id: 500, loss is: [1.9885247] -epoch: 3, batch_id: 1000, loss is: [2.3454063] -epoch: 3, batch_id: 1500, loss is: [1.9360502] -epoch: 4, batch_id: 0, loss is: [2.1657584] -epoch: 4, batch_id: 500, loss is: [2.0958445] -epoch: 4, batch_id: 1000, loss is: [1.9509046] -epoch: 4, batch_id: 1500, loss is: [1.8858738] -epoch: 5, batch_id: 0, loss is: [1.9648739] -epoch: 5, batch_id: 500, loss is: [1.8831095] -epoch: 5, batch_id: 1000, loss is: [1.9274123] -epoch: 5, batch_id: 1500, loss is: [2.2648232] -epoch: 6, batch_id: 0, loss is: [2.131785] -epoch: 6, batch_id: 500, loss is: [1.7363421] -epoch: 6, batch_id: 1000, loss is: [2.2151723] -epoch: 6, batch_id: 1500, loss is: [1.5245721] -epoch: 7, batch_id: 0, loss is: [1.7423642] -epoch: 7, batch_id: 500, loss is: [1.5562365] -epoch: 7, batch_id: 1000, loss is: [1.6524445] -epoch: 7, batch_id: 1500, loss is: [1.9120047] -epoch: 8, batch_id: 0, loss is: [1.8247225] -epoch: 8, batch_id: 500, loss is: [1.5704175] -epoch: 8, batch_id: 1000, loss is: [1.9273182] -epoch: 8, batch_id: 1500, loss is: [1.7724463] -epoch: 9, batch_id: 0, loss is: [1.5964721] -epoch: 9, batch_id: 500, loss is: [1.5145239] -epoch: 9, batch_id: 1000, loss is: [1.8208185] -epoch: 9, batch_id: 1500, loss is: [2.3465972] - -``` -# 6、模型预测： -前述的模型训练训练结束之后，就可以用该网络结构来计算出任意一张图片的高维向量表示（embedding)，通过计算该图片与图片库中其他图片的高维向量表示之间的相似度， -就可以按照相似程度进行排序，排序越靠前，则相似程度越高。 - -下面对测试集中所有的图片都两两计算相似度，然后选一部分相似的图片展示出来。 - -```python -near_neighbours_per_example = 10 - -x_test_t = paddle.to_tensor(x_test) -test_images_embeddings = model(x_test_t) -similarities_matrix = paddle.matmul(test_images_embeddings, test_images_embeddings, transpose_y=True) - -indicies = paddle.argsort(similarities_matrix, descending=True) -indicies = indicies.numpy() - -examples = np.empty( - ( - num_classes, - near_neighbours_per_example + 1, - 3, - height_width, - height_width, - ), - dtype=np.float32, -) - -for row_idx in range(num_classes): - examples_for_class = class_idx_to_test_idxs[row_idx] - anchor_idx = random.choice(examples_for_class) - - examples[row_idx, 0] = x_test[anchor_idx] - anchor_near_neighbours = indicies[anchor_idx][1:near_neighbours_per_example+1] - for col_idx, nn_idx in enumerate(anchor_near_neighbours): - examples[row_idx, col_idx + 1] = x_test[nn_idx] - -show_collage(examples) -``` -# 7、使用 @to_static 进行动静转换： -动静转换（@to_static）通过解析 Python 代码（抽象语法树，下简称：AST） 实现一行代码即可将动态图转为静态图的功能，只需在待转化的函数前添加一个装饰器 @paddle.jit.to_static - -使用 @to_static 即支持 可训练可部署 ，也支持只部署（详见模型导出） ，常见使用方式如下： - -方式一：使用 @to_static 装饰器装饰 SimpleNet (继承了 nn.Layer) 的 forward 函数: -```python -import paddle -from paddle.jit import to_static - -class SimpleNet(paddle.nn.Layer): - def __init__(self): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - - @to_static # 动静转换 - def forward(self, x, y): - out = self.linear(x) - out = out + y - return out - -net = SimpleNet() -net.eval() -x = paddle.rand([2, 10]) -y = paddle.rand([2, 3]) -out = net(x, y) # 动转静训练 -paddle.jit.save(net, './net') # 导出预测模型 -``` -方式二：调用 paddle.jit.to_static() 函数，仅做预测模型导出时推荐此种用法。 -```python -import paddle -from paddle.jit import to_static - -class SimpleNet(paddle.nn.Layer): - def __init__(self): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - - def forward(self, x, y): - out = self.linear(x) - out = out + y - return out - -net = SimpleNet() -net.eval() -net = paddle.jit.to_static(net) # 动静转换 -x = paddle.rand([2, 10]) -y = paddle.rand([2, 3]) -out = net(x, y) # 动转静训练 -paddle.jit.save(net, './net') # 导出预测模型 - -``` -方式一和方式二的主要区别是，前者直接在 forward() 函数定义处装饰，后者显式调用了 jit.to_static()方法，默认会对 net.forward 进行动静转换。 - -本实验使用 paddle.jit.to_static 实现动转静： -飞桨推荐使用 @paddle.jit.to_static 实现动转静，也被称为基于源代码转写的动态图转静态图，其基本原理是通过分析 Python 代码来将动态图代码转写为静态图代码，并在底层自动使用执行器运行，使用起来非常方便，只需要在原网络结构的 forward 前添加一个装饰器 paddle.jit.to_static 即可。 - -## 7.1、改写组网代码 -```python -class MyNet2(paddle.nn.Layer): - def __init__(self): - super(MyNet2, self).__init__() - - self.conv1 = paddle.nn.Conv2D(in_channels=3, - out_channels=32, - kernel_size=(3, 3), - stride=2) - - self.conv2 = paddle.nn.Conv2D(in_channels=32, - out_channels=64, - kernel_size=(3,3), - stride=2) - - self.conv3 = paddle.nn.Conv2D(in_channels=64, - out_channels=128, - kernel_size=(3,3), - stride=2) - - self.gloabl_pool = paddle.nn.AdaptiveAvgPool2D((1,1)) - - self.fc1 = paddle.nn.Linear(in_features=128, out_features=8) - - # 在 forward 前添加 paddle.jit.to_static 装饰器 - @paddle.jit.to_static() - def forward(self, x): - x = self.conv1(x) - x = F.relu(x) - x = self.conv2(x) - x = F.relu(x) - x = self.conv3(x) - x = F.relu(x) - x = self.gloabl_pool(x) - x = paddle.squeeze(x, axis=[2, 3]) - x = self.fc1(x) - x = x / paddle.norm(x, axis=1, keepdim=True) - return x -``` -通过 model.summary 查看网络结构。 -```python -model_2 = MyNet2() -model_info = paddle.summary(model_2, (10, 3, 32, 32)) -print(model_info) -``` - -```python -------------------------------------------------------------------------------- - Layer (type) Input Shape Output Shape Param # -=============================================================================== - Conv2D-4 [[10, 3, 32, 32]] [10, 32, 15, 15] 896 - Conv2D-5 [[10, 32, 15, 15]] [10, 64, 7, 7] 18,496 - Conv2D-6 [[10, 64, 7, 7]] [10, 128, 3, 3] 73,856 -AdaptiveAvgPool2D-2 [[10, 128, 3, 3]] [10, 128, 1, 1] 0 - Linear-2 [[10, 128]] [10, 8] 1,032 -=============================================================================== -Total params: 94,280 -Trainable params: 94,280 -Non-trainable params: 0 -------------------------------------------------------------------------------- -Input size (MB): 0.12 -Forward/backward pass size (MB): 0.89 -Params size (MB): 0.36 -Estimated Total Size (MB): 1.36 -------------------------------------------------------------------------------- - - -{'total_params': 94280, 'trainable_params': 94280} - -``` - -## 7.2、模型训练 -使用 paddle.jit.to_static 装饰器后，训练方式仍与原动态图训练一致。因此这里直接传入 model_2 完成模型的训练。 -```python -train(model_2) -``` - -```python -start training ... -epoch: 0, batch_id: 0, loss is: [2.2707999] -epoch: 0, batch_id: 500, loss is: [2.2578232] -epoch: 0, batch_id: 1000, loss is: [2.0026908] -epoch: 0, batch_id: 1500, loss is: [2.1243637] -epoch: 1, batch_id: 0, loss is: [2.5174978] -epoch: 1, batch_id: 500, loss is: [1.9235588] -epoch: 1, batch_id: 1000, loss is: [2.241912] -epoch: 1, batch_id: 1500, loss is: [2.2027836] -epoch: 2, batch_id: 0, loss is: [2.0674071] -epoch: 2, batch_id: 500, loss is: [1.8517029] -epoch: 2, batch_id: 1000, loss is: [1.9565346] -epoch: 2, batch_id: 1500, loss is: [2.2468033] -epoch: 3, batch_id: 0, loss is: [1.5385025] -epoch: 3, batch_id: 500, loss is: [2.1791337] -epoch: 3, batch_id: 1000, loss is: [2.0335] -epoch: 3, batch_id: 1500, loss is: [1.8313652] -epoch: 4, batch_id: 0, loss is: [1.8956888] -epoch: 4, batch_id: 500, loss is: [1.6906776] -epoch: 4, batch_id: 1000, loss is: [2.0118344] -epoch: 4, batch_id: 1500, loss is: [2.002913] -epoch: 5, batch_id: 0, loss is: [1.8000762] -epoch: 5, batch_id: 500, loss is: [1.7253144] -epoch: 5, batch_id: 1000, loss is: [1.5976737] -epoch: 5, batch_id: 1500, loss is: [1.5003413] -epoch: 6, batch_id: 0, loss is: [1.8904054] -epoch: 6, batch_id: 500, loss is: [2.1880364] -epoch: 6, batch_id: 1000, loss is: [2.0464098] -epoch: 6, batch_id: 1500, loss is: [1.7705017] -epoch: 7, batch_id: 0, loss is: [1.8255459] -epoch: 7, batch_id: 500, loss is: [1.8008741] -epoch: 7, batch_id: 1000, loss is: [1.9753224] -epoch: 7, batch_id: 1500, loss is: [2.376344] -epoch: 8, batch_id: 0, loss is: [1.7606968] -epoch: 8, batch_id: 500, loss is: [1.9435362] -epoch: 8, batch_id: 1000, loss is: [2.3564293] -epoch: 8, batch_id: 1500, loss is: [1.9458401] -epoch: 9, batch_id: 0, loss is: [1.7150522] -epoch: 9, batch_id: 500, loss is: [1.9844353] -epoch: 9, batch_id: 1000, loss is: [1.962418] -epoch: 9, batch_id: 1500, loss is: [1.7263882] - -``` -## 7.3、动转静模型导出 -动转静模块是架在动态图与静态图的一个桥梁，旨在打破动态图模型训练与静态部署的鸿沟，消除部署时对模型代码的依赖，打通与预测端的交互逻辑。 -在处理逻辑上，动转静主要包含两个主要模块： - -代码层面：将模型中所有的 layers 接口在静态图模式下执行以转为 Op ，从而生成完整的静态 Program - -Tensor 层面：将所有的 Parameters 和 Buffers 转为可导出的 Variable 参数（ persistable=True ） -通过 forward 导出预测模型 -通过 forward 导出预测模型导出一般包括三个步骤： - -切换 eval() 模式：类似 Dropout 、LayerNorm 等接口在 train() 和 eval() 的行为存在较大的差异，在模型导出前，请务必确认模型已切换到正确的模式，否则导出的模型在预测阶段可能出现输出结果不符合预期的情况。 - -构造 InputSpec 信息：InputSpec 用于表示输入的 shape、dtype、name 信息，且支持用 None 表示动态 shape（如输入的 batch_size 维度），是辅助动静转换的必要描述信息。 - -调用 save 接口：调用 paddle.jit.save 接口，若传入的参数是类实例，则默认对 forward 函数进行 @to_static 装饰，并导出其对应的模型文件和参数文件。 - -如下是一个简单的示例： - -```python -import paddle -from paddle.jit import to_static -from paddle.static import InputSpec - -class SimpleNet(paddle.nn.Layer): - def __init__(self): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - - def forward(self, x, y): - out = self.linear(x) - out = out + y - return out - - def another_func(self, x): - out = self.linear(x) - out = out * 2 - return out - -net = SimpleNet() -# train(net) 模型训练 (略) - -# step 1: 切换到 eval() 模式 -net.eval() - -# step 2: 定义 InputSpec 信息 -x_spec = InputSpec(shape=[None, 3], dtype='float32', name='x') -y_spec = InputSpec(shape=[3], dtype='float32', name='y') - -# step 3: 调用 jit.save 接口 -net = paddle.jit.save(net, path='simple_net', input_spec=[x_spec, y_spec]) # 动静转换 - -``` -本实验使用 paddle.jit.save 保存动转静模型 -使用 paddle.jit.to_static 转换模型后，需要调用 paddle.jit.save 将保存模型，以供后续的预测部署。保存后，会产生 model.pdmodel 、model.pdiparams.info、model.pdiparams 三个文件。 -```python -paddle.jit.save(model_2, 'model') -``` -使用 InputSpec 指定模型输入 Tensor 信息 -动静转换在生成静态图 Program 时，依赖输入 Tensor 的 shape、dtype 和 name 信息。因此，Paddle 提供了 InputSpec 接口，用于指定输入 Tensor 的描述信息，并支持动态 shape 特性。 - -构造 InputSpec -方式一：直接构造 - -InputSpec 接口在 paddle.static 目录下， 只有 shape 是必须参数， dtype 和 name 可以缺省，默认取值分别为 float32 和 None 。使用样例如下： -```python -from paddle.static import InputSpec - -x = InputSpec([None, 784], 'float32', 'x') -label = InputSpec([None, 1], 'int64', 'label') - -print(x) # InputSpec(shape=(-1, 784), dtype=paddle.float32, name=x) -print(label) # InputSpec(shape=(-1, 1), dtype=paddle.int64, name=label) - -``` -方式二：由 Tensor 构造 - -可以借助 InputSpec.from_tensor 方法，从一个 Tensor 直接创建 InputSpec 对象，其拥有与源 Tensor 相同的 shape 和 dtype 。 使用样例如下： -```python -import numpy as np -import paddle -from paddle.static import InputSpec - -x = paddle.to_tensor(np.ones([2, 2], np.float32)) -x_spec = InputSpec.from_tensor(x, name='x') -print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x) - -``` -注：若未在 from_tensor 中指定新的 name，则默认使用与源 Tensor 相同的 name。 - -方式三：由 numpy.ndarray - -也可以借助 InputSpec.from_numpy 方法，从一个 Numpy.ndarray 直接创建 InputSpec 对象，其拥有与源 ndarray 相同的 shape 和 dtype 。使用样例如下： -```python -import numpy as np -from paddle.static import InputSpec - -x = np.ones([2, 2], np.float32) -x_spec = InputSpec.from_numpy(x, name='x') -print(x_spec) # InputSpec(shape=(2, 2), dtype=paddle.float32, name=x) - -``` -注：若未在 from_numpy 中指定新的 name，则默认使用 None 。 -基本用法 -方式一： 在 @to_static 装饰器中调用 - -如下是一个简单的使用样例： - -```python -import paddle -from paddle.nn import Layer -from paddle.jit import to_static -from paddle.static import InputSpec - -class SimpleNet(Layer): - def __init__(self): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - - @to_static(input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]) - def forward(self, x, y): - out = self.linear(x) - out = out + y - return out - -net = SimpleNet() - -# save static graph model for inference directly -paddle.jit.save(net, './simple_net') - -``` -在上述的样例中， @to_static 装饰器中的 input_spec 为一个 InputSpec 对象组成的列表，用于依次指定参数 x 和 y 对应的 Tensor 签名信息。在实例化 SimpleNet 后，可以直接调用 paddle.jit.save 保存静态图模型，不需要执行任何其他的代码。 - -注： - -input_spec 参数中不仅支持 InputSpec 对象，也支持 int 、 float 等常见 Python 原生类型。 - -若指定 input_spec 参数，则需为被装饰函数的所有必选参数都添加对应的 InputSpec 对象，如上述样例中，不支持仅指定 x 的签名信息。 - -若被装饰函数中包括非 Tensor 参数，推荐函数的非 Tensor 参数设置默认值，如 forward(self, x, use_bn=False) - -方式二：在 to_static 函数中调用 - -若期望在动态图下训练模型，在训练完成后保存预测模型，并指定预测时需要的签名信息，则可以选择在保存模型时，直接调用 to_static 函数。使用样例如下： -```python -class SimpleNet(Layer): - def __init__(self): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - - def forward(self, x, y): - out = self.linear(x) - out = out + y - return out - -net = SimpleNet() - -# train process (Pseudo code) -for epoch_id in range(10): - train_step(net, train_reader) - -net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]) - -# save static graph model for inference directly -paddle.jit.save(net, './simple_net') - -``` -如上述样例代码中，在完成训练后，可以借助 to_static(net, input_spec=...) 形式对模型实例进行处理。Paddle 会根据 input_spec 信息对 forward 函数进行递归的动转静，得到完整的静态图，且包括当前训练好的参数数据。 - -方式三：通过 list 和 dict 推导 - -上述两个样例中，被装饰的 forward 函数的参数均为 Tensor 。这种情况下，参数个数必须与 InputSpec 个数相同。但当被装饰的函数参数为 list 或 dict 类型时，input_spec 需要与函数参数保持相同的嵌套结构。 - -当函数的参数为 list 类型时，input_spec 列表中对应元素的位置，也必须是包含相同元素的 InputSpec 列表。使用样例如下： -```python -class SimpleNet(Layer): - def __init__(self): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - - @to_static(input_spec=[[InputSpec(shape=[None, 10], name='x'), InputSpec(shape=[3], name='y')]]) - def forward(self, inputs): - x, y = inputs[0], inputs[1] - out = self.linear(x) - out = out + y - return out - -``` -其中 input_spec 参数是长度为 2 的 list ，对应 forward 函数的 x 和 bias_info 两个参数。 input_spec 的最后一个元素是包含键名为 x 的 InputSpec 对象的 dict ，对应参数 bias_info 的 Tensor 签名信息。 - -方式四：指定非 Tensor 参数类型 - -目前，to_static 装饰器中的 input_spec 参数仅接收 InputSpec 类型对象。若被装饰函数的参数列表除了 Tensor 类型，还包含其他如 Int、 String 等非 Tensor 类型时，推荐在函数中使用 kwargs 形式定义非 Tensor 参数，如下述样例中的 use_act 参数。 - - -```python - -class SimpleNet(Layer): - def __init__(self, ): - super(SimpleNet, self).__init__() - self.linear = paddle.nn.Linear(10, 3) - self.relu = paddle.nn.ReLU() - - def forward(self, x, use_act=False): - out = self.linear(x) - if use_act: - out = self.relu(out) - return out - -net = SimpleNet() -# 方式一：save inference model with use_act=False -net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x')]) -paddle.jit.save(net, path='./simple_net') - - -# 方式二：save inference model with use_act=True -net = to_static(net, input_spec=[InputSpec(shape=[None, 10], name='x'), True]) -paddle.jit.save(net, path='./simple_net') - -``` -在上述样例中，假设 step 为奇数时，use_act 取值为 False ； step 为偶数时， use_act 取值为 True 。动转静支持非 Tensor 参数在训练时取不同的值，且保证了取值不同的训练过程都可以更新模型的网络参数，行为与动态图一致。 - -在借助 paddle.jit.save 保存预测模型时，动转静会根据 input_spec 和 kwargs 的默认值保存推理模型和网络参数。建议将 kwargs 参数默认值设置为预测时的取值。 - -## 7.4、使用 paddle.jit.load 加载动转静模型 -将模型导出后，需要使用 paddle.jit.load 加载模型。加载后的模型可以直接用于预测。 -```python -model_2 = paddle.jit.load('model') -``` -## 7.5、使用动转静模型 -前述的模型训练训练结束之后，就可以用该网络结构来计算出任意一张图片的高维向量表示（embedding)，通过计算该图片与图片库中其他图片的高维向量表示之间的相似度，就可以按照相似程度进行排序，排序越靠前，则相似程度越高。 - -下面对测试集中所有的图片都两两计算相似度，然后选一部分相似的图片展示出来。 -```python -near_neighbours_per_example = 10 - -x_test_t = paddle.to_tensor(x_test) -test_images_embeddings = model_2(x_test_t) -similarities_matrix = paddle.matmul(test_images_embeddings, test_images_embeddings, transpose_y=True) - -indicies = paddle.argsort(similarities_matrix, descending=True) -indicies = indicies.numpy() - -examples = np.empty( - ( - num_classes, - near_neighbours_per_example + 1, - 3, - height_width, - height_width, - ), - dtype=np.float32, -) - -for row_idx in range(num_classes): - examples_for_class = class_idx_to_test_idxs[row_idx] - anchor_idx = random.choice(examples_for_class) - - examples[row_idx, 0] = x_test[anchor_idx] - anchor_near_neighbours = indicies[anchor_idx][1:near_neighbours_per_example+1] - for col_idx, nn_idx in enumerate(anchor_near_neighbours): - examples[row_idx, col_idx + 1] = x_test[nn_idx] - -show_collage(examples) -``` -通过上述的内容，就使用 @jit.to_static 完成了动转静并使用该模型进行了预测。 -# 8、 总结： -上述动态图转静态图的过程中，总体来说感觉还是可以的，具体总结了以下几个层面： - -- 1、接口层面： - 接口功能目前使用中覆盖了所有使用场景；InputSpec 指定信息也好用 -- 2、语法层面： - 语法支持方面尚未发现问题，我认为语法支持比较完备，但是图片展示功能（show_collage(examples)）在运行时展示不出来；控制流语法转换比较流畅 -- 3、报错层面： - 报错信息可读性差比较好，比如： - Traceback (most recent call last): - File "D:\Postgraduate\deep_learning\jiaoliu\pp\train.py", line 191, in - indicies = paddle.argsort(similarities_matrix, descending=True) - File "F:\Users\ASUS\anaconda3\envs\paddlepaddle-gpu\lib\site-packages\paddle\tensor\search.py", line 92, in argsort - _, ids = _C_ops.argsort(x, 'axis', axis, 'descending', descending) - SystemError: (Fatal) Operator argsort raises an struct paddle::memory::allocation::BadAlloc exception. - The exception content is - :ResourceExhaustedError: - - Out of memory error on GPU 0. Cannot allocate 762.939697MB memory on GPU 0, 3.256321GB memory has been allocated and available memory is only 761.527736MB. - - Please check whether there is any other process using GPU 0. - 1. If yes, please stop them, or start PaddlePaddle on another GPU. - 2. If no, please decrease the batch size of your model. - 但如果没有详细的提示信息还需上网查找解决方案；调试工具比较易用 -- 4、文档层面： - 从官方文档来说，动态图转静态图的示例文档感觉不太完善，例如 paddle.jit.load 等的 API 没有在使用指南的示例文档中展现，教程文档还有待完善，其他方面感觉内容比较详细丰富，具有较好的指导性 -- 5、意见建议： - 动态图转静态图的代码还是比较方便的，但对新手来说有一定难度，建议在使用指南中可以适当增加一些重点难点视频解说； - 有一些官网上的使用指南用于学习，但是有一些是在其使用指南中没有介绍的，例如数据集的拆分等这些需要自己去琢磨，可再丰富一下指南内容； - 使用指南中的方式或方法比较多的可以用序号组合一下，方便学习者更快的了解每种方式或方法的优缺点; - paddle 自带数据集太大，数据处理需消耗大量资源，使用起来不太方便，建议新增一些小的数据集。 diff --git "a/docs/eval/\343\200\220Hackathon No.112\343\200\221 PR.md" "b/docs/eval/\343\200\220Hackathon No.112\343\200\221 PR.md" deleted file mode 100644 index 2200ceb82c1..00000000000 --- "a/docs/eval/\343\200\220Hackathon No.112\343\200\221 PR.md" +++ /dev/null @@ -1,702 +0,0 @@ -# 深度体验飞桨分布式训练功能，并产出一份评估报告 - -| | | -| ------------ | ------------------------------------------------- | -| 提交作者 | mkm wjc | -| 提交时间 | 2022-04-02 | -| 版本号 | V1.0 | -| 依赖飞桨版本 | develop | -| 文件名 | 【Hackathon No.112】 PR.md | - -# 功能对比表格 - -| 功能对比表格 | | | | -| ------------ | ------------------------------------------------- | ------------------------------ | -------------------- | -| 类型 | 选项（选择一项） | 您的选择 | 使用时遇到的问题或建议 | -| 任务模式 | 单机多卡 or 多机多卡 | 单机多卡 | - | -| 运行方式 | 物理机 or docker or AI 开发平台 | docker | - | -| 任务类型 | 集合通信（GPU）or 参数服务器（CPU/GPU） | 集合通信（GPU） | - | -| 分布式训练使用程度 | 成功实践过 or 熟悉基本过程 or 了解基本过程 or 不熟悉 | 成功实践过 | 在使用曙光平台 docker 创建镜像后难以使用多节点进行分布式训练，建议推出一种可以直接在 E-Shell 安装的方式，可以使得任务通过队列的方式提交 | -| 分布式策略使用程度 | 使用过调优策略 or 了解部分优化策略 or 没有使用过优化策略 | 了解部分优化策略 | 无 | -| 分布式数据处理使用程度 | 熟悉 or 了解 or 不清楚 | 熟悉 | 无 | -| 分布式模型保存和加载使用程度 | 熟悉 or 了解 or 不清楚 | 熟悉 | 无 | -| 遇到问题如何解决（可多选） | 日志排查 or 社区反馈 or 提 Issue or 其他 | 日志排查、社区反馈 | 建议在官方文档中对常见问题进行汇总，便于使用者解决问题 | - - -# 一、摘要 - -本评估方案将从以下几个方面对 paddle 分布式框架进行评估： -- 1、环境配置，对比 pytorch 环境以及 paddle 环境在曙光超算上的配置 - 对曙光超算如何使用 paddle 进行分布式计算进行了介绍 - -- 2、Fleet API 的使用，对比 PyTorch API 与 Fleet API 的区别 - -- 3、分布式动态图的训练，使用 pytorch 和 paddle 完成在曙光超算上的分布式训练 - 对文档中代码进行的重写，导入了 paddle.vision 的部分包 - 对鲜花数据集加载的代码进行了修改，改为： -```python - train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) -``` -```python - optimizer.minimize(avg_loss)改为 optimizer.step() -``` - 鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。 - -- 4、文档质量评估，对 paddle 文档质量进行评估 - 文档中的代码有些旧，比如分布式训练快速开始中 1.3 动态图训练的代码许多 API 比较旧。 - 鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。在文档质量评估方面，认为文档对错误报告的解决方案不足。 - -- 5、错误汇总 - - -# 二、环境配置 - -## （1）在曙光超算昆山计算服务器部署 pytorch 分布式环境，给出部署步骤 -- 首先安装 anaconda -```python -bash Anaconda3-2020.07-Linux-x86_64.sh –u -``` -- 创建并进入 python3.6 环境 -```python -conda create -n pytorch-1.9 python=3.6 -conda activate pytorch-1.9 -``` -- 安装 pytorch-1.9（适配 rocm-4.0.1 及以上）PyTorch1.8 和 PyTorch1.9 安装 wheel 包在公共目录： -```python -/public/software/apps/DeepLearning/whl/rocm-4.0.1/ -``` -- 安装 pytorch_1.9-rocm_4.0.1(使用清华源) -```python -pip install /public/software/apps/DeepLearning/whl/rocm-4.0.1/torch-1.9.0+rocm4.0.1-cp36-cp36m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ -``` -- 对于 torchverion 的安装不能按照曙光官方给定的方法来，否则在 torchversion 在运行自定义算子时会出现错误，所以需要进行源码安装，安装方法如下: -```python -1、本地下载对应的 torchvision 分支源码包：https://github.com/pytorch/vision 上传集群， -2、进入对应的 conda 环境，加载对应的 rocm（这里 rocm4.0.1）版本； -3、conda install libpng -y -4、conda install jpeg -y -5、pip3 install numpy pillow matplotlib ninja -i https://pypi.tuna.tsinghua.edu.cn/simple/ -6、使用 salloc 申请计算结点，使用 ssh 登录至计算节点，并进入对应的 conda 环境加载 rocm（这里 rocm4.0.1），执行编译：CC=clang CXX=clang++ python setup.py install -``` -## （2）在曙光超算昆山计算服务器部署 paddle 分布式环境，给出部署步骤 -若在曙光平台不能使用 sudo 指令，从而导致不能直接使用 docker pull 方式安装镜像，可以直接使用曙光内置的可视化容器方式安装： -- 1、点击我的服务，计算智能服务 -- 2、点击容器服务 -- 3、点击容器管理添加镜像 -- 4、镜像添加，源镜像名称填：paddlepaddle/paddle，源镜像标签填：latest-dev-rocm4.0-miopen2.11 -- 5、添加完成后添加完成即可添加 -- 6、使用镜像，对镜像进行推送，快速访问选择，是 -- 7、点击 AI 服务，点击之前创建的容器 -- 8、点击启动容器，即可启动 rocm-4.0.1 环境 -- 9、打开容器后，该计算环境不能连接互联网，该环境可以调用此前终端设置的一切文件以及环境（包括 conda 环境），故需要提取打开命令行（E-Shell）创建一个 conda 环境安装 paddle。 -- 10、曙光服务器 conda 环境安装方式参考曙光超算官方链接： -```python -https://www.hpccube.com/doc/1.0.6/11250/general-handbook/compile/Anaconda.html -``` -- 11、激活 conda 环境并新建一个 conda 环境，进入该环境 -```python -source activate -conda create -n paddle python=3.7 -conda activate paddle -``` -- 12、在曙光上使用 paddle 官网给出的安装方式会出现错误。 -```python -python -m pip install paddlepaddle-rocm==2.2.2.rocm401.miopen211 -f https://www.paddlepaddle.org.cn/whl/rocm/stable.whl（此方法在曙光无法安装） -``` -- 13、故需要提前下载 whl 文件，下载链接： -```python -https://www.paddlepaddle.org.cn/whl/rocm/stable.whl -``` -- 14、paddlepaddle_rocm-2.2.2-cp37-cp37m-linux_x86_64.whl，版本经过测试可以安装。安装指令: -```python -pip install paddlepaddle_rocm-2.2.2-cp37-cp37m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ -``` -- 15、在安装完上述操作后还需要手动安装两个库 opencv-python 以及 scipy -```python -pip install scipy -i https://pypi.tuna.tsinghua.edu.cn/simple/ -pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple/ -``` - -## （3）对比两者的易用性与区别 -PyTorch 的分布式环境在曙光平台安装时需要手动编译 torchversion，这一点上 pytorch 比较繁琐。但是 pytorch 的环境在曙光平台比较稳定，而 paddle 环境在曙光平台经常不稳定，有时候能运行，有时候不能运行。 -![image](https://user-images.githubusercontent.com/102226413/164142960-e956efce-a8fe-40ea-bfba-a83b8f8203c5.png) - -上述问题是 rocm 版本问题，需要使用 rocm-4.0.1 版本。 修改 rocm 版本的方法为. module switch compiler/rocm/4.0.1 - -另外有一些问题没办法解决，我们使用的办法是重新开启镜像（多次开启后就会有可以使用的时候）无法解决的问题截图如下： -![image](https://user-images.githubusercontent.com/102226413/164143125-70d0e4ff-46d7-4461-8cb0-72c14e98b8e0.png) - -![image](https://user-images.githubusercontent.com/102226413/164143166-cde2793b-eb06-43a3-92d1-bfa68c2f1558.png) - - -另外，我们在曙光上使用 paddle 的方法为开启镜像的方式，但是曙光平台对 docker 镜像的支持不太好，每次镜像保持的时间最多为 72 小时，而且每次关闭镜像后，无法重新开启原先镜像。为了方便使用，希望能够支持 任务提交方式运行的 paddle 分布式框架。而且任务提交的方式还方便管理多节点运行。 - - -# 三、Fleet API 的使用 -## （1）分析 pytorch 分布式框架 DDP 某些 API 的使用 -- 导入必要的分布式训练依赖包 -```python -import torch.distributed as dist -``` -- 初始化 DDP 分布式环境，需要指定 backend, init_method,world_size, rank 四个参数 -```python -dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) -``` -- 优化器，不需要进行分布式函数的包装 -- 通过 DistributedDataParallel 获取分布式 model，用于支持分布式训练 -```python -model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank]) -``` - -## （2）按照文档内容使用 Fleet API -文档链接如下：https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/06_distributed_training/cluster_quick_start_cn.html -本次实验测试的 Fleet API 如下： -- 导入必要的分布式训练依赖包 -```python -from paddle.distributed import fleet -``` -- 初始化 Fleet 环境 -```python -fleet.init(is_collective=True) -``` -- 分布式优化器 -```python -optimizer = fleet.distributed_optimizer(optimizer) -``` -- 通过 Fleet API 获取分布式 model，用于支持分布式训练 -```python -resnet = fleet.distributed_model(resnet) -``` -## （3）比较 -pytorch 和 paddle 的分布式代码基本相似，pytorch 初始化 DDP 分布式环境，需要指定 backend, init_method,world_size, rank 四个参数，比较麻烦。 - -# 四、分布式动态图训练 - -## （1）使用 pytorch 完成一个图像分类的动态图分布式例子 - -DDP 分布式代码，测试 flower 数据集： -```python -import torch.nn as nn -import torch.utils.data as D -from torchvision import transforms -import torchvision -import torch -import os -import scipy.io as scio -import tarfile -import numpy as np -from PIL import Image -import torch.distributed as dist -MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': 'valid'} - - -class Flowers(torch.utils.data.Dataset): - def __init__(self, - data_file=None, - label_file=None, - setid_file=None, - mode='train', - transform=None, - backend=None): - assert mode.lower() in ['train', 'valid', 'test'], \ - "mode should be 'train', 'valid' or 'test', but got {}".format(mode) - - if backend is None: - backend = torchvision.get_image_backend() - self.backend = backend - - flag = MODE_FLAG_MAP[mode.lower()] - self.transform = transform - data_tar = tarfile.open(data_file) - self.data_path = data_file.replace(".tgz", "/") - if not os.path.exists(self.data_path): - os.mkdir(self.data_path) - data_tar.extractall(self.data_path) - - self.labels = scio.loadmat(label_file)['labels'][0] - self.indexes = scio.loadmat(setid_file)[flag][0] - - def __getitem__(self, idx): - index = self.indexes[idx] - label = np.array([self.labels[index - 1]]) - img_name = "jpg/image_%05d.jpg" % index - image = os.path.join(self.data_path, img_name) - image = Image.open(image) - if self.transform is not None: - image = self.transform(image) - - return image, label.astype('int64') - - def __len__(self): - return len(self.indexes) - - -def is_dist_avail_and_initialized(): - """检查是否支持分布式环境""" - if not dist.is_available(): - return False - if not dist.is_initialized(): - return False - return True - -def get_world_size(): - if not is_dist_avail_and_initialized(): - return 1 - return dist.get_world_size() - - -def reduce_value(value, average=True): - world_size = get_world_size() - if world_size < 2: # 单 GPU 的情况 - return value - - with torch.no_grad(): - dist.all_reduce(value) - if average: - value /= world_size - - return value - -def main(): - # 初始化 - rank = int(os.environ["RANK"]) - world_size = int(os.environ['WORLD_SIZE']) - gpu = int(os.environ['LOCAL_RANK']) - device = gpu - torch.cuda.set_device(gpu) - print("rank:", rank, "world size:", world_size, "gpu:", gpu) - dist.init_process_group(backend="nccl", world_size=world_size, rank=rank) - # 数据增强 - data_transforms = { - 'train': transforms.Compose([ - transforms.RandomRotation(45), # 随机旋转，-45 到 45 度之间随机 - transforms.CenterCrop(224), # 从中心开始裁剪 - transforms.RandomHorizontalFlip(p=0.5), # 随机水平翻转 选择一个概率 - transforms.RandomVerticalFlip(p=0.5), # 随机垂直翻转 - transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1), # 参数 1 为亮度，参数 2 为对比度，参数 3 为饱和度，参数 4 为色相 - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], - [0.229, 0.224, 0.225]) # 均值，标准差 - ]), - 'valid': transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], - [0.229, 0.224, 0.225]) - ]), - 'test': transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], - [0.229, 0.224, 0.225]) - ]) - } - batch_size = 32 - # 数据集构建与切分 - train_dataset = Flowers(mode='train', data_file="./102flowers.tgz", label_file="./imagelabels.mat", setid_file="./setid.mat", transform=data_transforms['train']) - train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) - train_loader = torch.utils.data.DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size) - # 模型构建 - model = torchvision.models.resnet50(pretrained=False, num_classes=102).to(device) - # 构建 DDP 模型 - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu]) - criterion = nn.CrossEntropyLoss() - optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.0001, momentum=0.9) - # 开始训练 - epochs = 100 - best_acc = 0.0 - for epoch in range(epochs): - if int(rank) == 0: - print('Epoch {}/{}'.format(epoch + 1, epochs)) - running_loss = 0.0 - running_corrects = 0 - train_sampler.set_epoch(epoch) - model.train() - for inputs, labels in train_loader: - inputs = inputs.to(device) - labels = labels.squeeze() - 1 - labels = labels.to(device) - optimizer.zero_grad() - outputs = model(inputs) - loss = criterion(outputs, labels) - if int(rank) == 0: - print("loss:", loss) - loss.backward() - optimizer.step() - _, preds = torch.max(outputs, 1) - running_loss += loss.item() - running_corrects += torch.sum(preds == labels) - running_corrects = reduce_value(running_corrects, average=False) - epoch_loss = running_loss / len(train_loader) - epoch_acc = 1.0 * running_corrects.item() / train_sampler.total_size - best_acc = max(best_acc, epoch_acc) - if int(rank) == 0: - print('{} Loss: {:.4f} Acc: {:.4f}'.format( - 'train', epoch_loss, epoch_acc)) - - if int(rank) == 0: - print('Training complete Best val Acc: {:4f}'.format(best_acc)) - -if __name__ == "__main__": - main() - -``` - -```python -python -m torch.distributed.launch --nproc_per_node=4 train.py -``` - -## （2）使用 paddle 完成一个图像分类的分布式例子 -由于文档中提供的代码出现了较多问题，我们对代码进行了重新的编写。修改的部分有： -- 1、导入了 paddle.vision 的部分包， -- 2、对鲜花数据集加载的代码进行了修改，改为： -- train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) - -官网的代码不能再曙光平台下载鲜花数据集，所以我们需要提取下载离线鲜花数据集报错如下： -![image](https://user-images.githubusercontent.com/102226413/164143513-24236f90-975d-47f1-a1db-7e18e6c94c9c.png) - -且数据集的保存地址为一个缓存空间，用户在使用的时候可能找不到数据集，如/public/home/username/.cache/paddle/dataset 目录。 -而 pytorch 的加载数据集 API 会吧数据集加载到当前目录，方便了使用者。 -![image](https://user-images.githubusercontent.com/102226413/164144065-2fea8ac3-dcf5-48ac-a4c7-05cace99c611.png) - - - -- 3、optimizer.minimize(avg_loss)改为 optimizer.step() -- 4、鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。 - -train_fleet_dygraph.py 重构后的代码： -```python -# -*- coding: UTF-8 -*- -import numpy as np -import paddle -# 导入必要分布式训练的依赖包 -from paddle.distributed import fleet -# 导入模型文件 -from paddle.vision.models import ResNet -from paddle.vision.models.resnet import BottleneckBlock -from paddle.io import Dataset, BatchSampler, DataLoader, DistributedBatchSampler -from paddle.vision.transforms import Compose -from paddle.vision.transforms import ToTensor -from paddle.vision.transforms import Resize -base_lr = 0.1 # 学习率 -momentum_rate = 0.9 # 冲量 -l2_decay = 1e-4 # 权重衰减 - -epoch = 10 # 训练迭代次数 -batch_size = 32 # 训练批次大小 -class_dim = 102 - - -# 设置数据读取器 -# def reader_decorator(reader): -# def __reader__(): -# for item in reader(): -# img = np.array(item[0]).astype('float32').reshape(3, 224, 224) -# label = np.array(item[1]).astype('int64').reshape(1) -# yield img, label -# -# return __reader__ - - -# 设置优化器 -def optimizer_setting(parameter_list=None): - optimizer = paddle.optimizer.Momentum( - learning_rate=base_lr, - momentum=momentum_rate, - weight_decay=paddle.regularizer.L2Decay(l2_decay), - parameters=parameter_list) - return optimizer - - -# 设置训练函数 -def train_resnet(): - # 初始化 Fleet 环境 - fleet.init(is_collective=True) - - # resnet = resnet34(class_dim=class_dim, layers=50) - resnet = ResNet(BottleneckBlock, 50, num_classes=class_dim) - - optimizer = optimizer_setting(parameter_list=resnet.parameters()) - # 分布式优化器 - optimizer = fleet.distributed_optimizer(optimizer) - # 通过 Fleet API 获取分布式 model，用于支持分布式训练 - resnet = fleet.distributed_model(resnet) - # 构建分布式数据集 归一化 / 255 并且转成 HWC --> CHW 格式 - # transform = ToTensor() - transform = Compose([ - Resize(size=(224, 224)), - ToTensor() - ]) - train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) - # 数据集的拆分 构建分布式数据集 - train_sampler = DistributedBatchSampler(train_dataset, 16, shuffle=True) - # , num_workers=2 - train_loader = DataLoader(train_dataset, batch_sampler=train_sampler) - - # train_reader = paddle.batch( - # reader_decorator(paddle.dataset.flowers.train(use_xmap=True)), - # # reader_decorator(paddle.vision.datasets.Cifar10(mode='train', backend='cv2')), - # batch_size=batch_size, - # drop_last=True) - # - # train_loader = paddle.io.DataLoader.from_generator( - # capacity=32, - # use_double_buffer=True, - # iterable=True, - # return_list=True, - # use_multiprocess=True) - - # train_loader.set_sample_list_generator(train_reader) - - for eop in range(epoch): - resnet.train() - for batch_id, data in enumerate(train_loader()): - img, label = data - label = label - 1 - label.stop_gradient = True - # 前向传播 - out = resnet(img) - loss = paddle.nn.functional.cross_entropy(input=out, label=label) - avg_loss = paddle.mean(x=loss) - acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1) - acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5) - dy_out = avg_loss.numpy() - avg_loss.backward() - # - optimizer.step() - resnet.clear_gradients() - if batch_id % 5 == 0: - print("[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f" % ( - eop, batch_id, dy_out, acc_top1, acc_top5)) - - -# 启动训练 -if __name__ == '__main__': - train_resnet() -``` - -运行方式： -```python -python3 -m paddle.distributed.launch --gpus=0,1,2,3 train_fleet_dygraph.py -``` - -## （3）两个程序的运行结果 - -- DDP 程序运行结果 -```python -Epoch 7/100 -loss: tensor(4.3571, device='cuda:0', grad_fn=) -loss: tensor(4.1223, device='cuda:0', grad_fn=) -loss: tensor(4.3427, device='cuda:0', grad_fn=) -loss: tensor(4.4527, device='cuda:0', grad_fn=) -loss: tensor(4.4157, device='cuda:0', grad_fn=) -loss: tensor(4.4089, device='cuda:0', grad_fn=) -loss: tensor(4.4046, device='cuda:0', grad_fn=) -loss: tensor(4.4396, device='cuda:0', grad_fn=) -loss: tensor(4.3414, device='cuda:0', grad_fn=) -loss: tensor(4.3834, device='cuda:0', grad_fn=) -loss: tensor(4.2856, device='cuda:0', grad_fn=) -loss: tensor(4.4135, device='cuda:0', grad_fn=) -loss: tensor(4.4047, device='cuda:0', grad_fn=) -loss: tensor(4.4164, device='cuda:0', grad_fn=) -loss: tensor(4.3926, device='cuda:0', grad_fn=) -loss: tensor(4.2654, device='cuda:0', grad_fn=) -loss: tensor(4.2372, device='cuda:0', grad_fn=) -loss: tensor(4.4528, device='cuda:0', grad_fn=) -loss: tensor(4.2135, device='cuda:0', grad_fn=) -loss: tensor(4.2939, device='cuda:0', grad_fn=) -loss: tensor(4.4517, device='cuda:0', grad_fn=) -loss: tensor(4.1285, device='cuda:0', grad_fn=) -loss: tensor(4.4465, device='cuda:0', grad_fn=) -loss: tensor(4.5095, device='cuda:0', grad_fn=) -loss: tensor(4.3783, device='cuda:0', grad_fn=) -loss: tensor(4.4235, device='cuda:0', grad_fn=) -loss: tensor(4.5945, device='cuda:0', grad_fn=) -loss: tensor(4.4677, device='cuda:0', grad_fn=) -loss: tensor(4.2633, device='cuda:0', grad_fn=) -loss: tensor(4.2308, device='cuda:0', grad_fn=) -loss: tensor(4.4648, device='cuda:0', grad_fn=) -loss: tensor(4.3647, device='cuda:0', grad_fn=) -loss: tensor(4.2926, device='cuda:0', grad_fn=) -loss: tensor(4.3164, device='cuda:0', grad_fn=) -loss: tensor(4.3357, device='cuda:0', grad_fn=) -loss: tensor(4.5612, device='cuda:0', grad_fn=) -loss: tensor(4.4285, device='cuda:0', grad_fn=) -loss: tensor(4.6838, device='cuda:0', grad_fn=) -loss: tensor(4.3026, device='cuda:0', grad_fn=) -loss: tensor(4.3654, device='cuda:0', grad_fn=) -loss: tensor(4.5110, device='cuda:0', grad_fn=) -loss: tensor(4.2723, device='cuda:0', grad_fn=) -loss: tensor(4.4866, device='cuda:0', grad_fn=) -loss: tensor(4.6240, device='cuda:0', grad_fn=) -loss: tensor(4.4451, device='cuda:0', grad_fn=) -loss: tensor(4.4438, device='cuda:0', grad_fn=) -loss: tensor(4.3717, device='cuda:0', grad_fn=) -loss: tensor(4.2463, device='cuda:0', grad_fn=) -loss: tensor(4.2591, device='cuda:0', grad_fn=) -train Loss: 4.3819 Acc: 0.0439 -Epoch 8/100 -loss: tensor(4.4381, device='cuda:0', grad_fn=) -loss: tensor(4.4635, device='cuda:0', grad_fn=) -loss: tensor(4.3493, device='cuda:0', grad_fn=) -loss: tensor(4.2414, device='cuda:0', grad_fn=) -loss: tensor(4.2735, device='cuda:0', grad_fn=) -loss: tensor(4.5272, device='cuda:0', grad_fn=) -loss: tensor(4.1665, device='cuda:0', grad_fn=) -loss: tensor(4.4553, device='cuda:0', grad_fn=) -loss: tensor(4.3675, device='cuda:0', grad_fn=) -loss: tensor(4.5145, device='cuda:0', grad_fn=) -loss: tensor(4.3524, device='cuda:0', grad_fn=) -loss: tensor(4.4013, device='cuda:0', grad_fn=) -loss: tensor(4.4338, device='cuda:0', grad_fn=) -loss: tensor(4.2520, device='cuda:0', grad_fn=) -loss: tensor(4.1550, device='cuda:0', grad_fn=) -loss: tensor(4.2074, device='cuda:0', grad_fn=) -loss: tensor(4.2859, device='cuda:0', grad_fn=) -loss: tensor(4.5531, device='cuda:0', grad_fn=) -loss: tensor(4.6937, device='cuda:0', grad_fn=) -loss: tensor(4.3387, device='cuda:0', grad_fn=) -loss: tensor(4.4027, device='cuda:0', grad_fn=) -loss: tensor(4.3194, device='cuda:0', grad_fn=) -loss: tensor(4.5824, device='cuda:0', grad_fn=) -loss: tensor(4.3335, device='cuda:0', grad_fn=) -loss: tensor(4.4477, device='cuda:0', grad_fn=) -loss: tensor(4.2184, device='cuda:0', grad_fn=) -loss: tensor(4.2377, device='cuda:0', grad_fn=) -loss: tensor(4.4388, device='cuda:0', grad_fn=) -loss: tensor(4.2814, device='cuda:0', grad_fn=) -loss: tensor(4.4338, device='cuda:0', grad_fn=) -loss: tensor(4.4741, device='cuda:0', grad_fn=) -loss: tensor(4.3977, device='cuda:0', grad_fn=) -loss: tensor(4.5670, device='cuda:0', grad_fn=) -loss: tensor(4.2727, device='cuda:0', grad_fn=) -loss: tensor(4.5136, device='cuda:0', grad_fn=) -loss: tensor(4.2349, device='cuda:0', grad_fn=) -loss: tensor(4.2736, device='cuda:0', grad_fn=) -loss: tensor(4.4575, device='cuda:0', grad_fn=) -loss: tensor(4.2906, device='cuda:0', grad_fn=) -loss: tensor(4.2580, device='cuda:0', grad_fn=) -loss: tensor(4.5590, device='cuda:0', grad_fn=) -loss: tensor(4.2204, device='cuda:0', grad_fn=) -loss: tensor(4.5499, device='cuda:0', grad_fn=) -loss: tensor(4.3762, device='cuda:0', grad_fn=) -loss: tensor(4.4189, device='cuda:0', grad_fn=) -loss: tensor(4.3005, device='cuda:0', grad_fn=) -loss: tensor(4.4099, device='cuda:0', grad_fn=) -loss: tensor(4.2168, device='cuda:0', grad_fn=) -loss: tensor(4.1337, device='cuda:0', grad_fn=) -train Loss: 4.3692 Acc: 0.0447 -``` - - -- paddlepaddle 运行结果，附了第 7、8 个 epoch -```python -launch train in GPU mode! -INFO 2022-04-02 19:30:53,752 launch_utils.py:510] Local start 4 processes. First process distributed environment info (Only For Debug): - +=======================================================================================+ - | Distributed Envs Value | - +---------------------------------------------------------------------------------------+ - | PADDLE_TRAINER_ID 0 | - | PADDLE_CURRENT_ENDPOINT 127.0.0.1:60409 | - | PADDLE_TRAINERS_NUM 4 | - | PADDLE_TRAINER_ENDPOINTS ... 0.1:41442,127.0.0.1:51971,127.0.0.1:52550| - | PADDLE_RANK_IN_NODE 0 | - | PADDLE_LOCAL_DEVICE_IDS 0 | - | PADDLE_WORLD_DEVICE_IDS 0,1,2,3 | - | FLAGS_selected_gpus 0 | - | FLAGS_selected_accelerators 0 | - +=======================================================================================+ - -INFO 2022-04-02 19:30:53,752 launch_utils.py:514] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 -launch proc_id:12642 idx:0 -launch proc_id:12645 idx:1 -launch proc_id:12648 idx:2 -launch proc_id:12651 idx:3 - -Epoch 7, batch 0] loss: 4.30898, acc1: 0.00000, acc5: 0.12500 -[Epoch 7, batch 5] loss: 4.07224, acc1: 0.06250, acc5: 0.18750 -[Epoch 7, batch 10] loss: 4.15420, acc1: 0.12500, acc5: 0.25000 -[Epoch 7, batch 15] loss: 4.47023, acc1: 0.06250, acc5: 0.31250 -[Epoch 7, batch 20] loss: 4.43729, acc1: 0.00000, acc5: 0.06250 -[Epoch 7, batch 25] loss: 4.22468, acc1: 0.00000, acc5: 0.12500 -[Epoch 7, batch 30] loss: 3.64519, acc1: 0.18750, acc5: 0.50000 -[Epoch 7, batch 35] loss: 4.45402, acc1: 0.06250, acc5: 0.12500 -[Epoch 7, batch 40] loss: 4.19298, acc1: 0.06250, acc5: 0.12500 -[Epoch 7, batch 45] loss: 3.99954, acc1: 0.00000, acc5: 0.37500 -[Epoch 7, batch 50] loss: 4.40063, acc1: 0.06250, acc5: 0.25000 -[Epoch 7, batch 55] loss: 4.24690, acc1: 0.00000, acc5: 0.25000 -[Epoch 7, batch 60] loss: 4.49993, acc1: 0.00000, acc5: 0.25000 -[Epoch 7, batch 65] loss: 4.41674, acc1: 0.00000, acc5: 0.06250 -[Epoch 7, batch 70] loss: 4.08913, acc1: 0.00000, acc5: 0.31250 -[Epoch 7, batch 75] loss: 4.19635, acc1: 0.00000, acc5: 0.12500 -[Epoch 7, batch 80] loss: 3.75817, acc1: 0.12500, acc5: 0.43750 -[Epoch 7, batch 85] loss: 4.29419, acc1: 0.06250, acc5: 0.25000 -[Epoch 7, batch 90] loss: 3.99528, acc1: 0.00000, acc5: 0.12500 -[Epoch 7, batch 95] loss: 4.32901, acc1: 0.06250, acc5: 0.37500 -[Epoch 8, batch 0] loss: 4.41281, acc1: 0.00000, acc5: 0.12500 -[Epoch 8, batch 5] loss: 4.10598, acc1: 0.12500, acc5: 0.25000 -[Epoch 8, batch 10] loss: 4.27404, acc1: 0.00000, acc5: 0.18750 -[Epoch 8, batch 15] loss: 3.97948, acc1: 0.06250, acc5: 0.06250 -[Epoch 8, batch 20] loss: 4.49495, acc1: 0.00000, acc5: 0.06250 -[Epoch 8, batch 25] loss: 4.07579, acc1: 0.00000, acc5: 0.25000 -[Epoch 8, batch 30] loss: 3.63573, acc1: 0.12500, acc5: 0.37500 -[Epoch 8, batch 35] loss: 3.79878, acc1: 0.18750, acc5: 0.31250 -[Epoch 8, batch 40] loss: 4.38518, acc1: 0.06250, acc5: 0.25000 -[Epoch 8, batch 45] loss: 4.08105, acc1: 0.12500, acc5: 0.25000 -[Epoch 8, batch 50] loss: 4.33881, acc1: 0.12500, acc5: 0.18750 -``` - -# 五、文档质量 -感觉文档中部分代码的版本较老，比如 1.3 动态图完整代码中： -```python -from resnet_dygraph import ResNet -``` -上述操作可以使用 paddle 内置 API 进行替换 -```python -from paddle.vision.models import ResNet -``` -flower 数据集在曙光平台不能通过 API 直接加载得到，需要手动下载。加载鲜花数据集的 API 也需要进行更新。可以更新为 -```python -train_dataset = paddle.vision.datasets.Flowers(mode='train', transform=transform) -``` -对于很多错误，在文档中没有一个很好的提示。可以对常见报错进行一个汇总。 - - -# 六、报错查错（问题汇总） -1、paddle 在曙光超算上运行出现 libamdhip64.4,，需要指定使用 rocm4.0.1 环境运行，曙光上 rocm-2.9 环境不能运行。 -```python -module rm compiler/rocm/2.9 -module load compiler/rocm/4.0.1 -``` - -![image](https://user-images.githubusercontent.com/102226413/164144235-ce808c51-1712-4417-b9bf-99da6362b3f0.png) - - -2、无法下载 flower 数据集，需要手动加载数据集 -按照文档旧 API 无法在曙光平台以及移动九天平台加载数据集，需要手动下载数据集。 -且数据集的保存地址为一个缓存空间，用户在使用的时候可能找不到数据集，如/public/home/username/.cache/paddle/dataset 目录。 -而 pytorch 的加载数据集 API 会吧数据集加载到当前目录，方便了使用者。 - -![image](https://user-images.githubusercontent.com/102226413/164143513-24236f90-975d-47f1-a1db-7e18e6c94c9c.png) - -![image](https://user-images.githubusercontent.com/102226413/164144065-2fea8ac3-dcf5-48ac-a4c7-05cace99c611.png) - -3、安装完 paddle 后运行该程序会缺少常用两个库：opencv-python 以及 scipy。 -安装方式： -```python -pip install scipy -i https://pypi.tuna.tsinghua.edu.cn/simple/ -pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple/ -``` -4、鲜花数据集的 label 索引是从 1 开始的，不是从 0 开始的，需要手工减 1。 -```python -img, label = data -label = label - 1 -``` - -5、未解决问题（无法在曙光上使用 paddle 的问题） -![image](https://user-images.githubusercontent.com/102226413/164143125-70d0e4ff-46d7-4461-8cb0-72c14e98b8e0.png) - -![image](https://user-images.githubusercontent.com/102226413/164143166-cde2793b-eb06-43a3-92d1-bfa68c2f1558.png) diff --git "a/docs/eval/\343\200\220Hackathon No.113\343\200\221 PR.md" "b/docs/eval/\343\200\220Hackathon No.113\343\200\221 PR.md" deleted file mode 100644 index b6bdcde327f..00000000000 --- "a/docs/eval/\343\200\220Hackathon No.113\343\200\221 PR.md" +++ /dev/null @@ -1,1139 +0,0 @@ - -# 1、任务描述： - - 飞桨框架于 2.0 正式版全面支持了动态图训练，并在 2.1、2.2 两个大版本中不断完善分布式能力，同时大幅增强了训练功能。在本任务中，我们希望能收到你对于飞桨动态图分布式训练功能的使用感受，可以与其他深度学习框架做功能对比，并产出一份对应的评估报告。 - -# 2、环境配置： - - 因为需要将飞桨 PaddlePaddle 框架的分布式与其他深度学习框架做功能对比，这里其他深度学习框架我选择了 PyTorch 框架，所以首先需要安装飞桨 PaddlePaddle 框架与 PyTorch 框架，开发平台选择了曙光平台昆山超算。 - -## 2.1、PyTorch 环境配置： - -- 1、首先安装 anaconda -```bash -bash Anaconda3-2020.07-Linux-x86_64.sh –u -``` -- 2、创建一个属于自己的环境并激活 -```bash -conda create --name pytorch_1.9 python=3.7 -conda activate pytorch-1.9 -``` -- 3、安装 pytorch-1.9（适配 rocm-4.0.1 及以上）PyTorch1.8 和 PyTorch1.9 安装 wheel 包在公共目录： -```bash -/public/software/apps/DeepLearning/whl/rocm-4.0.1/ -``` -- 安装指令如下： -```bash -module rm compiler/rocm/2.9 -module load compiler/rocm/4.0.1 -pip install /public/software/apps/DeepLearning/whl/rocm-4.0.1/torch-1.9.0+rocm4.0.1-cp36-cp36m-linux_x86_64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ -``` -- 对于 torchverion 的安装不能按照曙光官方帮助文档给定的方法来，否则 torchvision 在运行自定义算子时会出现错误，所以需要使用源码安装的方式，安装方法如下: -```text -1、本地下载对应的 torchvision 分支源码包：https://github.com/pytorch/vision 上传集群， -2、进入对应的 conda 环境，加载对应的 rocm（这里 rocm4.0.1）版本； -3、conda install libpng -y -4、conda install jpeg -y -5、pip3 install numpy pillow matplotlib ninja -i https://pypi.tuna.tsinghua.edu.cn/simple/ -6、使用 salloc 申请计算结点，使用 ssh 登录至计算节点，并进入对应的 conda 环境加载 rocm（这里 rocm4.0.1），执行编译：CC=clang CXX=clang++ python setup.py install -``` - -## 2.2、PaddlePaddle 环境配置： - -- PaddlePaddle 的环境在曙光超算上需要使用镜像的方式进行安装，镜像添加，源镜像名称填：paddlepaddle/paddle，源镜像标签填：latest-dev-rocm4.0-miopen2.11。然后创建实例打开容器。因为 Docker 容器中不能连接网络，使用 paddle 官网给出的安装方式会出现网络连接的错误。 -```bash -python -m pip install paddlepaddle-rocm==2.2.2.rocm401.miopen211 -f https://www.paddlepaddle.org.cn/whl/rocm/stable.whl -``` -- 故需要提前下载 whl 文件，下载链接如下，下载的版本为 paddlepaddle_rocm-2.1.1.rocm401.miopen211-cp37-cp37m-linux_x86_64.whl。下载链接： - -https://www.paddlepaddle.org.cn/whl/rocm/stable.whl - -- 安装指令为 -```bash -pip install paddlepaddle_rocm-2.1.1.rocm401.miopen211-cp37-cp37m-linux_x86_64.whl -``` -- 期间所需要的其他库都需要通过在曙光超算上通过 EShell 进行安装，需要设定清华镜像源，例如 -```bash -pip install six -i https://pypi.tuna.tsinghua.edu.cn/simple -``` -- 经过测试，测试指令为 -```bash -python -c "import paddle; paddle.utils.run_check()" -``` -环境有效。 - -## 2.3、安装对比分析 - -PyTorch 的分布式环境在曙光平台安装时需要手动源码编译 torchversion，这个过程比较慢，这一点上 PyTorch 相对比较繁琐；但是 PyTorch 的环境在曙光平台比较稳定，而 PaddlePaddle 环境在曙光平台不太稳定。 - -# 3、Paddle 单机与分布式： - -## 3.1、Paddle 单机 - -在图像处理中，关键点本质上是一种特征。它是对一个固定区域或者空间物理关系的抽象描述，描述的是一定邻域范围内的组合或上下文关系。它不仅仅是一个点信息，或代表一个位置，更代表着上下文与周围邻域的组合关系。关键点检测的目标就是通过计算机从图像中找出这些点的坐标，作为计算机视觉领域的一个基础任务，关键点的检测对于高级别任务，例如识别和分类具有至关重要的意义。任务选用的是人脸关键点检测，采用的方法是坐标点回归的方式进行，参考 PaddlePaddle 网址如下： -https://www.paddlepaddle.org.cn/documentation/docs/zh/practices/cv/landmark_detection.html - -其思路如下： -- 1、导入相关库 -- 2、构建数据集 -- 3、定义模型 -- 4、构建损失函数与优化器 -- 5、训练模型 -- 6、预测 - -具体分析如下： -- 第一步：导入相关库，即导入与本次任务相关的库，例如 import paddle -- 第二步：构建数据集，首先自定义一个处理人脸的 datasets，其继承于 Dataset 类，然后实现__init__,__getitem__与__len__函数，__init__函数实现一些数据集的初始化，即完成了对 csv 文件的读取与数据的清洗以及训练集、验证集、测试集的划分，__getitem__函数实现了通过 index 参数获取对应的 image 与 label，同时对图像进行预处理操作，即 transform 操作，__len__函数即返回数据集的数量（长度）。 -- 第三步：定义模型，构建一个模型的类，继承于 paddle.nn.Layer，然后实现__init__与 forward 函数，__init__函数定义网络模型的一些层结构，forward 函数实现网络模型的前向传播。同时，示例中给出的代码中网络模型经过了 paddle.Model 类的封装，paddle.Model 类相对来说会更方便一些，其内部定义了一些 fit 等函数可以直接使用。 -- 第四步：定义损失函数与优化器，优化器采用了 Adam 算法，同时初始学习率指定为 0.001，损失函数采用均方误差函数，因为网络模型经过了 paddle.Model 类的封装，所以此处对于损失函数与优化器需要使用 prepare 函数进行指定。 -- 第五步：构建训练代码，网络模型经过了 paddle.Model 类的封装，训练只需要调用其内部的 fit 函数，对于 fit 函数，其内部会把传入的数据集进行 DataLoader 封装，同时通过 tain_batch 函数进行其训练过程。 -- 第六步：测试，通过调用 paddle.Model 类的 predict 函数可以直接得到其预测的结果。 -- 总结：上述单机的过程中，其采用了 paddle.Model 类的封装，会在使用方面方便很多，并且 paddle.Model 类的内部会在模型训练的过程中加入一些回调函数 callback 进行使用，相对来说比较好用一些。 - -## 3.2、paddle 分布式的实现 - -### 方式一： -首先对于上述代码进行分析，在 paddle.Model 的底层可以发现其在对模型进行初始化的时候会判断是动态图的模式还是静态图的模式，即 -```python -if fluid.in_dygraph_mode(): - self._adapter = DynamicGraphAdapter(self) -else: - self._adapter = StaticGraphAdapter(self) -``` -因为我们采用的是动态图，所以其会调用第一个函数，即初始化 DynamicGraphAdapter(self)，观察其底层内部我们可以发现其会进行单机还是分布式的判断，如果是分布式，即 tasks>1，会初始化分布式的环境同时构建分布式的模型，代码分析如下： - -```python -if self._nranks > 1: - dist.init_parallel_env() - stradegy = fluid.dygraph.parallel.ParallelStrategy() - stradegy.nranks = ParallelEnv().nranks - stradegy.local_rank = ParallelEnv().local_rank - stradegy.trainer_endpoints = ParallelEnv().trainer_endpoints - stradegy.current_endpoint = ParallelEnv().current_endpoint - self.ddp_model = fluid.dygraph.parallel.DataParallel( - self.model.network, stradegy) - -``` -这样的话，分布式的环境与分布式的模型就已经在 paddle.Model 内的内部构建完成了。 -分析 model.prepare 即设置训练过程中的优化器与损失函数中我们发现其内部并没有设置分布式优化器，故这一部分需要自己在代码中添加，即 -```python -optim = fleet.distributed_optimizer(optim) -model.prepare(optim, paddle.nn.MSELoss()) -``` -分析训练的过程，即 model.fit 的底层代码，我们可以发现其底层构建数据集是就采用了分布式的数据集划分，即其 sampler 采用的是 DistributedBatchSampler，所以其训练时也就采用的是分布式的训练过程，即把数据划分到各个设备上进行训练。 - -总结：相对来说，示例中的代码使用了 paddle.Model 的封装，而其 paddle.Model 底层的一些函数中也添加了对分布式的处理，所以从示例代码中改成分布式的代码只需要进行分布式优化器的构建，即将 -```python -model.prepare(optim, paddle.nn.MSELoss()) -``` -改成 -```python -optim = fleet.distributed_optimizer(optim) -model.prepare(optim, paddle.nn.MSELoss()) -``` -### 方式二： -paddle.Model 类内部封装的东西使用起来比较方便，但是不利于用户了解单机转成分布式的具体流程，所以我没有使用 paddle.Model 类，而是重新编写了分布式的代码。其流程如下： - -- 1、导入分布式所需要的依赖包 -- 2、初始化分布式环境 -- 3、构建模型 -- 4、设置分布式所需要的优化器 -- 5、数据集的拆分 -- 6、构建训练代码 -- 7、启动分布式任务 - -下面具体流程如下： -- 第一步：导入分布式所需要的依赖包，即导入任务相关的 API 函数，例如 -```python -from paddle.distributed import fleet -``` -- 第二步：初始化分布式环境，采用了 collective 通信，代码如下: -```python -fleet.init(is_collective=True) -``` -- 第三步：构建网络模型，这个网络模型采用了和示例单机中相同的网络模型。代码如下： -```python -model = FaceNet(num_keypoints=15) -model = fleet.distributed_model(model) -``` -- 第四步：设置分布式所需要的优化器，优化器采用了 Adam 优化器，初始学习率为 0.001，代码如下： -```python -optim = paddle.optimizer.Adam(learning_rate=1e-3, parameters=model.parameters()) - optim = fleet.distributed_optimizer(optim) -``` -- 第五步：数据集的拆分 -对于分布式的数据拆分，需要先构建其数据集的采样器，这里需要使用 DistributedBatchSampler，其中参数为数据集 dataset、batch_size、num_replicas、rank、shuffle、drop_last，这里我指定了 dataset、batch_size、shuffle，设置了 shuffle 为 True，即对数据进行打乱，其中的参数 num_replicas 如果不指定，其默认会获取当前环境中的 ntasks，然后按照 ntasks 分配数据集。drop_last 参数如果不指定会默认为 False，也就是不会丢失最后一个 batch 的数据。构建完分布式采样器之后，使用 DataLoader 进行封装一下，这里指定一下 batch_sampler 为刚才构建的采样器，注意指定 batch_sampler 参数之后不需要再指定 batch_size、shuffle 以及 drop_last 参数。 -- 第六步：构建训练代码 -这里不采用 paddle.Model 进行封装，所以需要自己编写 for 循环获取数据进行前向传播以及反向传播的过程，代码如下： -```python -for eop in range(epoch): - # train_sampler.set_epoch(eop) - # 设置为训练模式 - model.train() - for batch_id, data in enumerate(train_loader()): - img, label = data - label.stop_gradient = True - # 前向传播 - out = model(img) - # 均方损失函数 - loss = paddle.nn.functional.mse_loss(input=out, label=label) - loss_data = loss.numpy() - # 反向传播 - loss.backward() - optim.step() - model.clear_gradients() - if batch_id % 10 == 0: - print("[Epoch %d, batch %d] loss: %.5f" % (eop, batch_id, loss_data)) -``` -* 第七步： -飞桨通过 paddle.distributed.launch 组件启动分布式任务。该组件可用于启动单机多卡分布式任务，也可以用于启动多机多卡分布式任务。该组件为每张参与分布式任务的训练卡启动一个训练进程。默认情形下，该组件将在每个节点上启动 N 个进程，这里 N 等于训练节点的卡数，即使用所有的训练卡。用户也可以通过 gpus 参数指定训练节点上使用的训练卡列表，该列表以逗号分隔。需要注意的是，所有节点需要使用相同数量的训练卡数。为了启动多机分布式任务，需要通过 ips 参数指定所有节点的 IP 地址列表，该列表以逗号分隔。需要注意的是，该列表在所有节点上需要保持一致，即各节点 IP 地址出现的顺序需要保持一致。这里我进行了单机多卡与多机多卡的实验，实验启动方式如下： - -单机多卡分布式任务：这里我采用的是四个卡，启动方式如下： -```python -python -m paddle.distributed.launch --gpus 0,1,2,3 train_multi_gpu.py -``` -运行结果如下： -```python ------------ Configuration Arguments ----------- -gpus: 0,1,2,3 -heter_worker_num: None -heter_workers: -http_port: None -ips: 127.0.0.1 -log_dir: log -nproc_per_node: None -run_mode: None -server_num: None -servers: -training_script: train_multi_4.py -training_script_args: [] -worker_num: None -workers: ------------------------------------------------- -WARNING 2022-04-02 23:29:34,154 launch.py:359] Not found distinct arguments and compiled with cuda or xpu. Default use collective mode -launch train in GPU mode! -INFO 2022-04-02 23:29:34,156 launch_utils.py:510] Local start 4 processes. First process distributed environment info (Only For Debug): - +=======================================================================================+ - | Distributed Envs Value | - +---------------------------------------------------------------------------------------+ - | PADDLE_TRAINER_ID 0 | - | PADDLE_CURRENT_ENDPOINT 127.0.0.1:40344 | - | PADDLE_TRAINERS_NUM 4 | - | PADDLE_TRAINER_ENDPOINTS ... 0.1:41302,127.0.0.1:54290,127.0.0.1:60414| - | PADDLE_RANK_IN_NODE 0 | - | PADDLE_LOCAL_DEVICE_IDS 0 | - | PADDLE_WORLD_DEVICE_IDS 0,1,2,3 | - | FLAGS_selected_gpus 0 | - | FLAGS_selected_accelerators 0 | - +=======================================================================================+ - -INFO 2022-04-02 23:29:34,156 launch_utils.py:514] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 -launch proc_id:22388 idx:0 -launch proc_id:22391 idx:1 -launch proc_id:22394 idx:2 -launch proc_id:22397 idx:3 -W0402 23:29:36.404554 22388 gen_comm_id_helper.cc:120] connect addr=127.0.0.1:41302 failed 1 times with reason: Connection refused retry after 0.5 seconds -I0402 23:29:36.905133 22388 nccl_context.cc:74] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 0 -W0402 23:30:20.059859 22388 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 90.0, Driver API Version: 321.0, Runtime API Version: 3.1 -W0402 23:30:20.067070 22388 device_context.cc:417] device: 0, MIOpen Version: 2.11.0 -I0402 23:30:20.082139 22388 nccl_context.cc:107] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 10 - -/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. -Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations - if data.dtype == np.object: -2022-04-02 23:32:10,565-INFO: [topology.py:152:__init__] HybridParallelInfo: rank_id: 0, dp_degree: 4, mp_degree: 1, pp_degree: 1, dp_group: [0, 1, 2, 3], mp_group: [0], pp_group: [0], check/clip group: [0] -Epoch 1 / 30 -/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance. - "When training, we now always track global mean and variance.") -step 0 / 14 - loss: 0.91819 -step 10 / 14 - loss: 0.06710 -Eval begin... -step 0 / 4 - loss: 0.02265 -Eval samples: 428 -Epoch 2 / 30 -step 0 / 14 - loss: 0.04950 -step 10 / 14 - loss: 0.02599 -Eval begin... -step 0 / 4 - loss: 0.00559 -Eval samples: 428 -Epoch 3 / 30 -step 0 / 14 - loss: 0.02373 -step 10 / 14 - loss: 0.01906 -Eval begin... -step 0 / 4 - loss: 0.00270 -Eval samples: 428 -Epoch 4 / 30 -step 0 / 14 - loss: 0.01631 -step 10 / 14 - loss: 0.01318 -Eval begin... -step 0 / 4 - loss: 0.00227 -Eval samples: 428 -Epoch 5 / 30 -step 0 / 14 - loss: 0.01354 -step 10 / 14 - loss: 0.01125 -Eval begin... -step 0 / 4 - loss: 0.00136 -Eval samples: 428 -Epoch 6 / 30 -step 0 / 14 - loss: 0.01162 -step 10 / 14 - loss: 0.00890 -Eval begin... -step 0 / 4 - loss: 0.00145 -Eval samples: 428 -Epoch 7 / 30 -step 0 / 14 - loss: 0.01274 -step 10 / 14 - loss: 0.01234 -Eval begin... -step 0 / 4 - loss: 0.00259 -Eval samples: 428 -Epoch 8 / 30 -step 0 / 14 - loss: 0.00965 -step 10 / 14 - loss: 0.00664 -Eval begin... -step 0 / 4 - loss: 0.00200 -Eval samples: 428 -Epoch 9 / 30 -step 0 / 14 - loss: 0.00763 -step 10 / 14 - loss: 0.00707 -Eval begin... -step 0 / 4 - loss: 0.00129 -Eval samples: 428 -Epoch 10 / 30 -step 0 / 14 - loss: 0.00697 -step 10 / 14 - loss: 0.00591 -Eval begin... -step 0 / 4 - loss: 0.00150 -Eval samples: 428 -Epoch 11 / 30 -step 0 / 14 - loss: 0.00708 -step 10 / 14 - loss: 0.00488 -Eval begin... -step 0 / 4 - loss: 0.00376 -Eval samples: 428 -Epoch 12 / 30 -step 0 / 14 - loss: 0.00859 -step 10 / 14 - loss: 0.00602 -Eval begin... -step 0 / 4 - loss: 0.00262 -Eval samples: 428 -Epoch 13 / 30 -step 0 / 14 - loss: 0.00815 -step 10 / 14 - loss: 0.00786 -Eval begin... -step 0 / 4 - loss: 0.00187 -Eval samples: 428 -Epoch 14 / 30 -step 0 / 14 - loss: 0.00540 -step 10 / 14 - loss: 0.00839 -Eval begin... -step 0 / 4 - loss: 0.00235 -Eval samples: 428 -Epoch 15 / 30 -step 0 / 14 - loss: 0.00568 -step 10 / 14 - loss: 0.00725 -Eval begin... -step 0 / 4 - loss: 0.00189 -Eval samples: 428 -Epoch 16 / 30 -step 0 / 14 - loss: 0.00657 -step 10 / 14 - loss: 0.00842 -Eval begin... -step 0 / 4 - loss: 0.00112 -Eval samples: 428 -Epoch 17 / 30 -step 0 / 14 - loss: 0.00573 -step 10 / 14 - loss: 0.00642 -Eval begin... -step 0 / 4 - loss: 0.00297 -Eval samples: 428 -Epoch 18 / 30 -step 0 / 14 - loss: 0.00617 -step 10 / 14 - loss: 0.00632 -Eval begin... -step 0 / 4 - loss: 0.00399 -Eval samples: 428 -Epoch 19 / 30 -step 0 / 14 - loss: 0.00850 -step 10 / 14 - loss: 0.00664 -Eval begin... -step 0 / 4 - loss: 0.00083 -Eval samples: 428 -Epoch 20 / 30 -step 0 / 14 - loss: 0.00555 -step 10 / 14 - loss: 0.01062 -Eval begin... -step 0 / 4 - loss: 0.00103 -Eval samples: 428 -Epoch 21 / 30 -step 0 / 14 - loss: 0.00480 -step 10 / 14 - loss: 0.00547 -Eval begin... -step 0 / 4 - loss: 0.00085 -Eval samples: 428 -Epoch 22 / 30 -step 0 / 14 - loss: 0.00463 -step 10 / 14 - loss: 0.00429 -Eval begin... -step 0 / 4 - loss: 0.00088 -Eval samples: 428 -Epoch 23 / 30 -step 0 / 14 - loss: 0.00439 -step 10 / 14 - loss: 0.00575 -Eval begin... -step 0 / 4 - loss: 0.00302 -Eval samples: 428 -Epoch 24 / 30 -step 0 / 14 - loss: 0.00447 -step 10 / 14 - loss: 0.00492 -Eval begin... -step 0 / 4 - loss: 0.00134 -Eval samples: 428 -Epoch 25 / 30 -step 0 / 14 - loss: 0.00526 -step 10 / 14 - loss: 0.00549 -Eval begin... -step 0 / 4 - loss: 0.00141 -Eval samples: 428 -Epoch 26 / 30 -step 0 / 14 - loss: 0.00387 -step 10 / 14 - loss: 0.00477 -Eval begin... -step 0 / 4 - loss: 0.00243 -Eval samples: 428 -Epoch 27 / 30 -step 0 / 14 - loss: 0.00668 -step 10 / 14 - loss: 0.00480 -Eval begin... -step 0 / 4 - loss: 0.00094 -Eval samples: 428 -Epoch 28 / 30 -step 0 / 14 - loss: 0.00388 -step 10 / 14 - loss: 0.00392 -Eval begin... -step 0 / 4 - loss: 0.00090 -Eval samples: 428 -Epoch 29 / 30 -step 0 / 14 - loss: 0.00384 -step 10 / 14 - loss: 0.00503 -Eval begin... -step 0 / 4 - loss: 0.00099 -Eval samples: 428 -Epoch 30 / 30 -step 0 / 14 - loss: 0.00454 -step 10 / 14 - loss: 0.00398 -Eval begin... -step 0 / 4 - loss: 0.00083 -Eval samples: 428 -INFO 2022-04-02 23:34:34,617 launch.py:268] Local processes completed. -``` -多机多卡分布式任务：相对于单机多卡的分布式任务，多机多卡下不需要对代码有任何的更改，故不需要更改程序，只需要改变一下启动方式。这里对于多机的情况，我在曙光超算上开启了两个镜像，每个镜像申请了 2 个加速卡，开启之后首先使用 ifconfig 查看两个镜像下的 ip 地址，然后使用 ping 指令查看一下两个镜像能否相互 ping 通，然后分别在两个镜像下使用下面指令运行：（其中 ips 中的两个 ip 地址换成镜像中的 ip 地址） -```python -python -m paddle.distributed.launch --ips="192.168.0.1,192.168.0.2" --gpus 0,1 train_fleet_dygraph.py -``` -运行结果如下： -```python ------------ Configuration Arguments ----------- -gpus: 0,1 -heter_worker_num: None -heter_workers: -http_port: None -ips: 173.8.206.5,173.15.88.4 -log_dir: log -nproc_per_node: None -run_mode: None -server_num: None -servers: -training_script: train_multi_4.py -training_script_args: [] -worker_num: None -workers: ------------------------------------------------- -INFO 2022-04-03 16:58:50,318 launch.py:348] Run collective mode. gpu arguments:['--ips'], cuda count:2 -launch train in GPU mode! -INFO 2022-04-03 16:58:50,320 launch_utils.py:510] Local start 2 processes. First process distributed environment info (Only For Debug): - +=======================================================================================+ - | Distributed Envs Value | - +---------------------------------------------------------------------------------------+ - | PADDLE_TRAINER_ID 2 | - | PADDLE_CURRENT_ENDPOINT 173.15.88.4:6070 | - | PADDLE_TRAINERS_NUM 4 | - | PADDLE_TRAINER_ENDPOINTS ... .5:6071,173.15.88.4:6070,173.15.88.4:6071| - | PADDLE_RANK_IN_NODE 0 | - | PADDLE_LOCAL_DEVICE_IDS 0 | - | PADDLE_WORLD_DEVICE_IDS 0,1,0,1 | - | FLAGS_selected_gpus 0 | - | FLAGS_selected_accelerators 0 | - +=======================================================================================+ - -INFO 2022-04-03 16:58:50,321 launch_utils.py:514] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0 -launch proc_id:226 idx:0 -launch proc_id:229 idx:1 -W0403 16:58:55.397342 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 1 times with reason: Connection refused retry after 0.5 seconds -W0403 16:58:55.913834 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 2 times with reason: Connection refused retry after 1 seconds -W0403 16:58:56.914870 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 3 times with reason: Connection refused retry after 1.5 seconds -W0403 16:58:58.419081 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 4 times with reason: Connection refused retry after 2 seconds -W0403 16:59:00.419550 342 gen_comm_id_helper.cc:120] connect addr=173.15.88.4:6070 failed 5 times with reason: Connection refused retry after 2.5 seconds -I0403 16:59:00.498201 226 gen_comm_id_helper.cc:181] Server listening on: 173.15.88.4:6070 successful. -I0403 16:59:02.923162 342 nccl_context.cc:74] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 0 -I0403 16:59:02.922478 226 nccl_context.cc:74] init nccl context nranks: 4 local rank: 2 gpu id: 0 ring id: 0 -W0403 16:59:03.412863 226 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 90.0, Driver API Version: 321.0, Runtime API Version: 3.1 -W0403 16:59:03.413836 342 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 90.0, Driver API Version: 321.0, Runtime API Version: 3.1 -W0403 16:59:03.421044 226 device_context.cc:417] device: 0, MIOpen Version: 2.11.0 -W0403 16:59:03.431119 342 device_context.cc:417] device: 0, MIOpen Version: 2.11.0 -I0403 16:59:03.452879 342 nccl_context.cc:107] init nccl context nranks: 4 local rank: 0 gpu id: 0 ring id: 10 -I0403 16:59:03.452358 226 nccl_context.cc:107] init nccl context nranks: 4 local rank: 2 gpu id: 0 ring id: 10 -/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. -Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations - if data.dtype == np.object: -/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/tensor/creation.py:125: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. -Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations - if data.dtype == np.object: -2022-04-03 16:59:03,590-INFO: [topology.py:152:__init__] HybridParallelInfo: rank_id: 0, dp_degree: 4, mp_degree: 1, pp_degree: 1, dp_group: [0, 1, 2, 3], mp_group: [0], pp_group: [0], check/clip group: [0] -2022-04-03 16:59:03,590-INFO: [topology.py:152:__init__] HybridParallelInfo: rank_id: 2, dp_degree: 4, mp_degree: 1, pp_degree: 1, dp_group: [0, 1, 2, 3], mp_group: [2], pp_group: [2], check/clip group: [2] -Epoch 1 / 30 -Epoch 1 / 30 -/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance. - "When training, we now always track global mean and variance.") -/public/home/ac48p2il5w/anaconda3/envs/paddle_wjc_task/lib/python3.7/site-packages/paddle/nn/layer/norm.py:641: UserWarning: When training, we now always track global mean and variance. - "When training, we now always track global mean and variance.") -step 0 / 14 - loss: 0.85710step 0 / 14 - loss: 0.84205 - -step 10 / 14 - loss: 0.05992step 10 / 14 - loss: 0.05753 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.01912 -step 0 / 4 - loss: 0.01973 -Eval samples: 428 -Epoch 2 / 30 -Eval samples: 428 -Epoch 2 / 30 -step 0 / 14 - loss: 0.04044step 0 / 14 - loss: 0.03697 - -step 10 / 14 - loss: 0.02107 -step 10 / 14 - loss: 0.02008 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00549 -step 0 / 4 - loss: 0.00503 -Eval samples: 428 -Epoch 3 / 30 -Eval samples: 428 -Epoch 3 / 30 -step 0 / 14 - loss: 0.01858step 0 / 14 - loss: 0.02017 - -step 10 / 14 - loss: 0.01667 -step 10 / 14 - loss: 0.01475 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00300 -step 0 / 4 - loss: 0.00350 -Eval samples: 428 -Epoch 4 / 30 -Eval samples: 428 -Epoch 4 / 30 -step 0 / 14 - loss: 0.01376step 0 / 14 - loss: 0.01363 - -step 10 / 14 - loss: 0.01104 -step 10 / 14 - loss: 0.01042 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00240 -step 0 / 4 - loss: 0.00249 -Eval samples: 428 -Epoch 5 / 30 -Eval samples: 428 -Epoch 5 / 30 -step 0 / 14 - loss: 0.01193 -step 0 / 14 - loss: 0.01152 -step 10 / 14 - loss: 0.01055 -step 10 / 14 - loss: 0.01039 -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00166 -step 0 / 4 - loss: 0.00154 -Eval samples: 428 -Epoch 6 / 30 -Eval samples: 428 -Epoch 6 / 30 -step 0 / 14 - loss: 0.01019step 0 / 14 - loss: 0.00879 - -step 10 / 14 - loss: 0.00636 -step 10 / 14 - loss: 0.00875 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00257 -step 0 / 4 - loss: 0.00269 -Eval samples: 428 -Epoch 7 / 30 -Eval samples: 428 -Epoch 7 / 30 -step 0 / 14 - loss: 0.00884 -step 0 / 14 - loss: 0.00832 -step 10 / 14 - loss: 0.01013step 10 / 14 - loss: 0.00839 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00227 -step 0 / 4 - loss: 0.00179 -Eval samples: 428 -Epoch 8 / 30 -Eval samples: 428 -Epoch 8 / 30 -step 0 / 14 - loss: 0.00957step 0 / 14 - loss: 0.00895 - -step 10 / 14 - loss: 0.00707step 10 / 14 - loss: 0.00848 - -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00154 -step 0 / 4 - loss: 0.00165 -Eval samples: 428 -Epoch 9 / 30 -Eval samples: 428 -Epoch 9 / 30 -step 0 / 14 - loss: 0.00606 -step 0 / 14 - loss: 0.00653 -step 10 / 14 - loss: 0.00564 -step 10 / 14 - loss: 0.00885 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00180 -step 0 / 4 - loss: 0.00198 -Eval samples: 428 -Epoch 10 / 30 -Eval samples: 428 -Epoch 10 / 30 -step 0 / 14 - loss: 0.00697step 0 / 14 - loss: 0.00953 - -step 10 / 14 - loss: 0.00577 -step 10 / 14 - loss: 0.00713 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00132 -step 0 / 4 - loss: 0.00098 -Eval samples: 428 -Epoch 11 / 30 -Eval samples: 428 -Epoch 11 / 30 -step 0 / 14 - loss: 0.00611 -step 0 / 14 - loss: 0.00576 -step 10 / 14 - loss: 0.00705step 10 / 14 - loss: 0.00557 - -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00229 -step 0 / 4 - loss: 0.00296 -Eval samples: 428 -Epoch 12 / 30 -Eval samples: 428 -Epoch 12 / 30 -step 0 / 14 - loss: 0.00789step 0 / 14 - loss: 0.00599 - -step 10 / 14 - loss: 0.00526 -step 10 / 14 - loss: 0.00603 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00172 -step 0 / 4 - loss: 0.00214 -Eval samples: 428 -Epoch 13 / 30 -Eval samples: 428 -Epoch 13 / 30 -step 0 / 14 - loss: 0.00730 -step 0 / 14 - loss: 0.00736 -step 10 / 14 - loss: 0.00490step 10 / 14 - loss: 0.00524 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00129step 0 / 4 - loss: 0.00103 - -Eval samples: 428 -Epoch 14 / 30 -Eval samples: 428 -Epoch 14 / 30 -step 0 / 14 - loss: 0.00460step 0 / 14 - loss: 0.00474 - -step 10 / 14 - loss: 0.01072step 10 / 14 - loss: 0.01057 - -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00576 -step 0 / 4 - loss: 0.00567 -Eval samples: 428 -Epoch 15 / 30 -Eval samples: 428 -Epoch 15 / 30 -step 0 / 14 - loss: 0.00579 -step 0 / 14 - loss: 0.00819 -step 10 / 14 - loss: 0.00540step 10 / 14 - loss: 0.00567 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00103 -step 0 / 4 - loss: 0.00086 -Eval samples: 428 -Epoch 16 / 30 -Eval samples: 428 -Epoch 16 / 30 -step 0 / 14 - loss: 0.00486 -step 0 / 14 - loss: 0.00365 -step 10 / 14 - loss: 0.00576step 10 / 14 - loss: 0.00696 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00199 -step 0 / 4 - loss: 0.00195 -Eval samples: 428 -Epoch Eval samples: 17428 -/Epoch 3017 - / 30 -step 0 / 14 - loss: 0.00727 -step 0 / 14 - loss: 0.00616 -step 10 / 14 - loss: 0.00570step 10 / 14 - loss: 0.00670 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00239 -step 0 / 4 - loss: 0.00206 -Eval samples: 428 -Epoch 18 / 30 -Eval samples: 428 -Epoch 18 / 30 -step 0 / 14 - loss: 0.00725step 0 / 14 - loss: 0.00614 - -step 10 / 14 - loss: 0.00709 -step 10 / 14 - loss: 0.00581 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00101 -step 0 / 4 - loss: 0.00106 -Eval samples: Eval samples: 428428 - -Epoch Epoch 1919 // 3030 - -step 0 / 14 - loss: 0.00431 -step 0 / 14 - loss: 0.00360 -step 10 / 14 - loss: 0.00403 -step 10 / 14 - loss: 0.00461 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00208 -step 0 / 4 - loss: 0.00230 -Eval samples: 428 -Epoch 20 / 30 -Eval samples: 428 -Epoch 20 / 30 -step 0 / 14 - loss: 0.00542step 0 / 14 - loss: 0.00424 - -step 10 / 14 - loss: 0.00426 -step 10 / 14 - loss: 0.00520 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00098 -step 0 / 4 - loss: 0.00088 -Eval samples: 428 -Epoch 21 / 30 -Eval samples: 428 -Epoch 21 / 30 -step 0 / 14 - loss: 0.00452 -step 0 / 14 - loss: 0.00440 -step 10 / 14 - loss: 0.00489step 10 / 14 - loss: 0.00504 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00619 -step 0 / 4 - loss: 0.00630 -Eval samples: 428 -Epoch 22 / 30 -Eval samples: 428 -Epoch 22 / 30 -step 0 / 14 - loss: 0.00808 -step 0 / 14 - loss: 0.00936 -step 10 / 14 - loss: 0.00833step 10 / 14 - loss: 0.00734 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00144 -step 0 / 4 - loss: 0.00146 -Eval samples: 428 -Epoch 23 / 30 -Eval samples: 428 -Epoch 23 / 30 -step 0 / 14 - loss: 0.00359 -step 0 / 14 - loss: 0.00316 -step 10 / 14 - loss: 0.00343step 10 / 14 - loss: 0.00360 - -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00134 -step 0 / 4 - loss: 0.00156 -Eval samples: 428 -Epoch 24 / 30 -Eval samples: 428 -Epoch 24 / 30 -step 0 / 14 - loss: 0.00492 -step 0 / 14 - loss: 0.00398 -step 10 / 14 - loss: 0.00439step 10 / 14 - loss: 0.00321 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00180 -step 0 / 4 - loss: 0.00185 -Eval samples: 428 -Epoch 25 / 30 -Eval samples: 428 -Epoch 25 / 30 -step 0 / 14 - loss: 0.00396step 0 / 14 - loss: 0.00419 - -step 10 / 14 - loss: 0.00630step 10 / 14 - loss: 0.00398 - -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00308step 0 / 4 - loss: 0.00300 - -Eval samples: 428 -Epoch 26 / 30 -Eval samples: 428 -Epoch 26 / 30 -step 0 / 14 - loss: 0.00535 -step 0 / 14 - loss: 0.00487 -step 10 / 14 - loss: 0.00413step 10 / 14 - loss: 0.00418 - -Eval begin...Eval begin... - -step 0 / 4 - loss: 0.00072 -step 0 / 4 - loss: 0.00091 -Eval samples: 428 -Epoch 27 / 30 -Eval samples: 428 -Epoch 27 / 30 -step 0 / 14 - loss: 0.00315step 0 / 14 - loss: 0.00386 - -step 10 / 14 - loss: 0.00435step 10 / 14 - loss: 0.00286 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00116 -step 0 / 4 - loss: 0.00125 -Eval samples: 428 -Epoch 28 / 30 -Eval samples: 428 -Epoch 28 / 30 -step 0 / 14 - loss: 0.00396step 0 / 14 - loss: 0.00281 - -step 10 / 14 - loss: 0.00449step 10 / 14 - loss: 0.00416 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00104 -step 0 / 4 - loss: 0.00141 -Eval samples: 428 -Epoch 29 / 30 -Eval samples: 428 -Epoch 29 / 30 -step 0 / 14 - loss: 0.00542step 0 / 14 - loss: 0.00310 - -step 10 / 14 - loss: 0.00570 -step 10 / 14 - loss: 0.00425 -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00171 -step 0 / 4 - loss: 0.00193 -Eval samples: 428 -Epoch 30 / 30 -Eval samples: 428 -Epoch 30 / 30 -step 0 / 14 - loss: 0.00446step 0 / 14 - loss: 0.00518 - -step 10 / 14 - loss: 0.00387step 10 / 14 - loss: 0.00282 - -Eval begin... -Eval begin... -step 0 / 4 - loss: 0.00094 -step 0 / 4 - loss: 0.00079 -Eval samples: 428 -Eval samples: 428 -``` - -## 3.3 评估表格 -| | | | | | -| :------------ | ---------- | --------------- | ------ | ------| -| 序号 | 核心步骤 | 完成情况（成功/不成功） | 遇到问题 |解决方法（无法解决请注明）| -| 1 | 导入分布式训练所需要的依赖包 | 完成 | 无 | 无 | -| 2 | 初始化分布式环境 | 完成 | PaddlePaddle 安装有时候会有一些问题、NCCL 初始化有问题![图片](https://user-images.githubusercontent.com/35827074/165877509-b84f5846-b175-4ab9-8ae3-eef66ed09047.png) | 使用 export 设置一些安装的库的环境变量，上述问题是 rocm 版本问题，需要使用 rocm-4.0.1 版本。 修改 rocm 版本的方法为. module switch compiler/rocm/4.0.1，再就是导入超算上的一些环境变量 export NCCL_IB_HCA=mlx5_0 export NCCL_SOCKET_IFNAME=eno1 export NCCL_IB_DISABLE=0 | -| 3 | 设置分布式训练需要的优化器 | 完成 | 无 | 无 | -| 4 | 数据集拆分 | 完成 | 示例里面没有数据集的拆分案例，不会使用数据集的拆分；使用 DistributedBatchSampler 采样器之后 DataLoader 中无法指定 batchsize 以及 shuffle 参数 | 分析 paddle 的分布式 API 底层以及结合其他深度学习框架分析，发现了 DistributedBatchSampler API，然后分析其底层实现，发现可以应用；分析 DataLoader 底层的源码，发现在指定 batch_sampler 参数之后不能指定 batchsize、shuffle 以及 drop_last 参数，然后在 DistributedBatchSampler 构建采样器的过程中指定。分布式数据集拆分使用 DistributedBatchSampler，通过使用 DistributedBatchSampler 构建一个分布式的采样器，其会将数据平均划分到多个设备中，然后将其输入到 Dataloader 函数中，参数为 batch_sampler，案例的全部代码已经在附录中给出。关于拆分部分如下：train_sampler = DistributedBatchSampler(train_dataset, 32, shuffle=True) train_loader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=2) val_sampler = DistributedBatchSampler(val_dataset, 32) val_loader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=2) | -| 5 | 构建训练代码 | 完成 | 无 | 无 | -| 6 | 单机多卡分布式训练 | 完成 | 在曙光超算上使用 SBATCH 作业提交方式时有环境的问题 | 申请 4 个 DCU，使用镜像的方式进行实现 | -| 7 | 多机多卡分布式训练 | 完成 | 无 | 注意再进行多机多卡时先要两个机器之间互相 ping 一下 | - -* 总结：上述单机转为分布式的过程中，总体来说感觉还是可以的，动态图下 paddle 单机转为分布式的代码还是比较方便的，也有一些官网上的参考文档用于学习，但是有一些是在其参考文档中没有介绍的，例如数据集的拆分等这些需要自己去思考。 - -# 4、PyTorch 单机与分布式： -## 4.1、PyTorch 单机 - -PyTorch 单机下的流程和 Paddle 单机下的流程基本上是相似的，采用了和 Paddle 单机下相同的模型，相同的优化器与损失函数，过程如下： -- 1、导入相关库 -- 2、构建数据集 -- 3、定义模型 -- 4、构建损失函数与优化器 -- 5、训练模型 -- 6、预测 - -## 4.2、PyTorch 分布式 - -PyTorch 单机转为分布式的具体流程如下： -- 1、导入分布式所需要的依赖包 -```python -import torch.distributed as dist -``` -- 2、初始化分布式环境，需要指定一下通信后端（我采用的是 NCCL），初始化方法（我采用的是 env 初始化），当前进程号以及总的进程数量。 -```python -rank = int(os.environ["RANK"]) -world_size = int(os.environ['WORLD_SIZE']) -gpu = int(os.environ['LOCAL_RANK']) -device = gpu -torch.cuda.set_device(gpu) -dist.init_process_group(backend="nccl", world_size=world_size, rank=rank) -``` -3、构建模型，这里需要使用 torch.nn.parallel.DistributedDataParallel 构建其分布式的模型，这里没有采用 Dataparallel API，是因为其效率相对于 DistributedDataParallel 比较低。 -```python -model = FaceNet(num_keypoints=15).to(device) -model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[gpu]) -``` -4、构建优化器与损失函数 -优化器采用 SGD，指定学习率与动量等参数。 -```python -optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.0001, momentum=0.9) -``` -损失函数采用均方误差损失函数。 -```python -criterion = torch.nn.MSELoss() -``` -5、数据集的拆分，首先使用 DistributedSampler 构建分布式数据集的拆分，这里可以指定一下是否需要进行 shuffle 以及 drop_last 等参数，然后使用 DataLoader 进行封装。 -```python -train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True) -train_loader = torch.utils.data.DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size) -val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False) -val_loader = torch.utils.data.DataLoader(val_dataset, sampler=val_sampler, batch_size=batch_size) -``` -6、构建训练代码，这里需要使用 train_sampler.set_epoch(epoch)设置一下，因为在 train_sampler 中使用了 shuffle 这个参数，而看其源码可以发现其依靠 self.epoch 这个参数进行随机种子的设置，所以需要在每个 epoch 训练时设置一下采样器的 self.epoch 这个参数，即通过 train_sampler.set_epoch(epoch)进行设置。训练代码如下，每一个 epoch 训练完进行 val 的验证评估： -```python -for epoch in range(total_epoch): - model.train() - train_sampler.set_epoch(epoch) - for batch_id, data in enumerate(train_loader): - inputs, labels = data - inputs = inputs.to(device) - labels = labels.to(device) - optimizer.zero_grad() - outputs = model(inputs) - loss = criterion(outputs, labels) - loss.backward() - optimizer.step() - if batch_id % 10 == 0: - print("step %d / %d - loss: %.5f" % (batch_id, len(train_loader), loss.item())) - model.eval() - print("Eval begin...") - for batch_id, data in enumerate(val_loader): - inputs, labels = data - inputs = inputs.to(device) - labels = labels.to(device) - optimizer.zero_grad() - outputs = model(inputs) - loss = criterion(outputs, labels) - if batch_id % 10 == 0: - print("step %d / %d - loss: %.5f" % (batch_id, len(val_loader), loss.item())) - print("Eval samples: ", len(val_dataset)) -``` -7、启动分布式任务 -PyTorch 分布式下两种启动方式，我选择的是和 paddle 类似的一种方式即通过 torch.distributed.launch 进行启动，启动方式如下： -```bash -python -m torch.distributed.launch --nproc_per_node=4 train_multi.py -``` -# 4、对比分析 - -下面对 PaddlePaddle 与 PyTorch 单机转为分布式进行对比性分析： -## 相似点： -PaddlePaddle 与 PyTorch 单机转为分布式的流程基本上是相似的，基本上遵循如下流程：导入分布式相关的库、初始化分布式环境、构建分布式的模型、构建优化器与损失函数、同时进行分布式数据集的拆分，最后构建训练代码，其整个流程都比较相似，相对来说 PaddlePaddle 与 PyTorch 单机转为分布式都是比较方便的。 -## 不同点： -- 1、PaddlePaddle 内部有许多封装好的类，例如 paddle.Model 类，其内部封装了好多函数，例如 train_batch/fit 等函数，还加入了一些回调函数例如 EarlyStopping 等，可以比较方便地进行训练、测试的过程，比较容易使用。 -- 2、对于单机转为分布式的过程，如果对数据集进行 shuffle 打乱时，PyTorch 需要在每个 epoch 训练开始时调用 train_sampler.set_epoch 函数即设置一下 shuffle 打乱的种子，但是 PaddlePaddle 如果对数据集进行 shuffle 打乱时，可以选择并不需要设置，因为其内部在每次打乱时会将 self.epoch 进行加一的操作，即自动改变了其数据打乱的种子，使用起来更加方便。 -- 3、从使用方面来说，PaddlePaddle 的分布式初始化有时候会报错有时候能使用，其环境用起来感觉不太稳定，PyTorch 的分布式使用起来相对比较稳定，其初始化环境等功能实现都比较稳定。 -- 4、从官方文档来说，PaddlePaddle 的分布式示例文档中感觉不太完善，例如 DistributedSampler 等的 API 没有在分布式示例文档中展现，paddle.Model 等 API 没有找到相关 API 文档的介绍；PyTorch 的分布式示例文档相对来说比较完善，包括其示例以及 API 的使用以及分布式通信的相关 API 都有其文档介绍。 - -# 5、附录 -## 单机示例转为分布式的代码 -```python -import numpy as np -import pandas as pd - -import paddle -from paddle.io import Dataset -from paddle.vision.transforms import transforms -from paddle.vision.models import resnet18 -# 导入必要分布式训练的依赖包 -from paddle.distributed import fleet -from paddle.io import Dataset, BatchSampler, DataLoader, DistributedBatchSampler - -# 数据文件的路径 -# training.csv: 包含了用于训练的人脸关键点坐标和图像。 -# test.csv: 包含了用于测试的人脸关键点图像, 没有标注关键点坐标。 -# IdLookupTable.csv: 测试集关键点的位置的对应名称。 -Train_Dir = './data/data60/training.csv' -Test_Dir = './data/data60/test.csv' -lookid_dir = './data/data60/IdLookupTable.csv' - - -class ImgTransforms: - """ - 图像预处理工具，用于将图像进行升维(96, 96) => (96, 96, 3)， - 并对图像的维度进行转换从 HWC 变为 CHW - """ - - def __init__(self, fmt): - self.format = fmt - - def __call__(self, img): - if len(img.shape) == 2: - img = np.expand_dims(img, axis=2) - img = img.transpose(self.format) - - if img.shape[0] == 1: - img = np.repeat(img, 3, axis=0) - return img - - -class FaceDataset(Dataset): - def __init__(self, data_path, mode='train', val_split=0.2): - self.mode = mode - assert self.mode in ['train', 'val', 'test'], \ - "mode should be 'train' or 'test', but got {}".format(self.mode) - # 读取数据的路径 - self.data_source = pd.read_csv(data_path) - # 清洗数据, 数据集中有很多样本只标注了部分关键点, 这里有两种策略 - # 第一种, 将未标注的位置从上一个样本对应的关键点复制过来 - # self.data_source.fillna(method = 'ffill',inplace = True) - # 第二种, 将包含有未标注的样本从数据集中移除 - self.data_source.dropna(how="any", inplace=True) - self.data_label_all = self.data_source.drop('Image', axis=1) - - # 划分训练集和验证集合 - if self.mode in ['train', 'val']: - np.random.seed(43) - data_len = len(self.data_source) - # 随机划分 - shuffled_indices = np.random.permutation(data_len) - # 顺序划分 - # shuffled_indices = np.arange(data_len) - self.shuffled_indices = shuffled_indices - val_set_size = int(data_len * val_split) - if self.mode == 'val': - val_indices = shuffled_indices[:val_set_size] - self.data_img = self.data_source.reindex().iloc[val_indices] - self.data_label = self.data_label_all.reindex().iloc[val_indices] - elif self.mode == 'train': - train_indices = shuffled_indices[val_set_size:] - self.data_img = self.data_source.reindex().iloc[train_indices] - self.data_label = self.data_label_all.reindex().iloc[train_indices] - elif self.mode == 'test': - self.data_img = self.data_source - self.data_label = self.data_label_all - - self.transforms = transforms.Compose([ - ImgTransforms((2, 0, 1)) - ]) - - # 每次迭代时返回数据和对应的标签 - def __getitem__(self, idx): - img = self.data_img['Image'].iloc[idx].split(' ') - img = ['0' if x == '' else x for x in img] - img = np.array(img, dtype='float32').reshape(96, 96) - # 图像预处理操作 - img = self.transforms(img) - label = np.array(self.data_label.iloc[idx, :], dtype='float32') / 96 - return img, label - - # 返回整个数据集的总数 - def __len__(self): - return len(self.data_img) - -# 模型的定义 -# 对应 30 维度 -class FaceNet(paddle.nn.Layer): - def __init__(self, num_keypoints, pretrained=False): - super(FaceNet, self).__init__() - self.backbone = resnet18(pretrained) - self.outLayer1 = paddle.nn.Sequential( - paddle.nn.Linear(1000, 512), - paddle.nn.ReLU(), - paddle.nn.Dropout(0.1)) - self.outLayer2 = paddle.nn.Linear(512, num_keypoints*2) - - def forward(self, inputs): - out = self.backbone(inputs) - out = self.outLayer1(out) - out = self.outLayer2(out) - return out - - -def main(): - # 初始化 Fleet 环境 - fleet.init(is_collective=True) - # 训练数据集和验证数据集 - train_dataset = FaceDataset(Train_Dir, mode='train') - val_dataset = FaceDataset(Train_Dir, mode='val') - # 测试数据集 - test_dataset = FaceDataset(Test_Dir, mode='test') - # 初始化模型 - model = FaceNet(num_keypoints=15) - # 优化器的设置 - optim = paddle.optimizer.Adam(learning_rate=1e-3, parameters=model.parameters()) - # 构建分布式优化器 - optim = fleet.distributed_optimizer(optim) - # 通过 Fleet API 获取分布式 model，用于支持分布式训练 - model = fleet.distributed_model(model) - # 数据集的拆分 构建分布式数据集 - train_sampler = DistributedBatchSampler(train_dataset, 32, shuffle=True) - train_loader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=2) - val_sampler = DistributedBatchSampler(val_dataset, 32) - val_loader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=2) - epoch = 30 - for eop in range(epoch): - # train_sampler.set_epoch(eop) - # 设置为训练模式 - model.train() - print("Epoch ", eop + 1, "/", epoch) - for batch_id, data in enumerate(train_loader()): - img, label = data - label.stop_gradient = True - # 前向传播 - out = model(img) - # 均方损失函数 - loss = paddle.nn.functional.mse_loss(input=out, label=label) - loss_data = loss.numpy() - # 反向传播 - loss.backward() - optim.step() - model.clear_gradients() - if batch_id % 10 == 0: - print("step %d / %d - loss: %.5f" % (batch_id, len(train_loader), loss_data)) - model.eval() - print("Eval begin...") - for batch_id, data in enumerate(val_loader()): - img, label = data - label.stop_gradient = True - # 前向传播 - out = model(img) - # 均方损失函数 - loss = paddle.nn.functional.mse_loss(input=out, label=label) - loss_data = loss.numpy() - if batch_id % 10 == 0: - print("step %d / %d - loss: %.5f" % (batch_id, len(val_loader), loss_data)) - print("Eval samples: ", len(val_dataset)) - - -if __name__ == "__main__": - main() -``` diff --git "a/docs/eval/\343\200\220Hackathon No.69\343\200\221PR.md" "b/docs/eval/\343\200\220Hackathon No.69\343\200\221PR.md" deleted file mode 100644 index a631f9bc315..00000000000 --- "a/docs/eval/\343\200\220Hackathon No.69\343\200\221PR.md" +++ /dev/null @@ -1,1095 +0,0 @@ -# 飞桨动态图单机训练功能评估报告 - -| 领域 | 飞桨动态图单机训练功能评估报告 | -| ------------ | ------------------------------ | -| 提交作者 | 王源 袁闯闯 | -| 提交时间 | 2022-05-03 | -| 版本号 | V1.0 | -| 依赖飞桨版本 | paddlepaddle-gpu==2.2 | -| 文件名 | 【Hackathon No.69】 PR.md | - - -# 一、摘要 - -相关背景：飞桨框架于 2.0 正式版全面支持了动态图训练，并在 2.1、2.2 两个大版本中不断新增了 API 以及大幅增强了训练功能。希望有人对于飞桨框架动态图下单机训练功能整体的使用感受，可以与其他深度学习框架做功能对比，包括 API、Tensor 索引、NumPy Compatibility、报错信息提示、训练性能、以及各种 trick 的用法等，并产出一份对应的评估报告。 - -本评估方案将从以下几个方面对 paddle 动态图单机训练功能进行体验评估： - -1、环境配置及开启动态图模式 - -2、API 使用及对比 - -调用高层 API:如：paddle.Model、paddle.vision，与 pytorch 框架做对比。并在 LeNet、ResNet 等网络模型或模型自己组网（Sequential 组网、SubClass 组网）训练中进行评估。 - -3、Tensor 索引 - -在模型训练中体验了 Tensor 在数据传递过程中的表现（如：了解索引和 其切片规则、访问与修改 Tensor、逻辑相关函数重写规则），并体验了使用指南里有关 Tensor 的所有基本操作。 - -4、NumPy 兼容性分析及对比 - -在动态图模型代码中，所有与组网相关的 numpy 操作都必须用 paddle 的 API 重新实现，所以在模型训练过程中体验 Paddle.API 来感受对比 PyTorch 的表现；分析了 Tensor 兼容 Numpy 数组的同时，优先使用 Tensor 的两种场景。 - -5、动态图单机训练 - -体验控制流和共享权重的使用效果，然后在数据集定义、加载和数据预处理、数据增强方面感受与 PyTorch 使用的区别，最后通过 LeNet 举例说明训练结果，并进行了对比分析 - -6、各种 trick 的用法体验 - -7、报错汇总 - -# 二、环境配置及开启动态图模式 - -本次训练评估在个人电脑上进行： - -| 名称 | 参数 | -| :------: | :------------------------------------------: | -| CPU | Intel(R)Core(TM)i5-7200U CPU @2.50GHz | -| 内存 | 12GB DDR4 | -| GPU | NVIDIA GeForce 940MX | -| 系统平台 | Window 10 家庭中文版（64 位） | -| 软件环境 | Paddle2.2、 PyTorch3.8、Cuda 10.1、Anaconda3 | - -Paddle 环境安装参考： https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/windows-pip.html - -安装 CPU 版本时候使用到了清华镜像源：pip install -i https://pypi.tuna.tsinghua.edu.cn/simple paddlepaddle - -GPU 版本：python -m pip install paddlepaddle-gpu==2.2.2.post101 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html - -Paddle 与 PyTorch 环境配置使用对比： 在单机安装中，都安装在了 conda 环境，Paddle 安装比较顺利，直接按照文档安装即可，与 PyTorch 安装没有太大区别，单机测试稳定性也都比较良好。 - - - -# 三、API 使用及对比 - -## 1、从 PaddlePaddle 的各个 API 目录来对比分析 - -首先，基础操作类、组网类、Loss 类、工具类、视觉类这五大类从映射 PyTorch 上看，在单机训练中可以满足训练及预测所需的 API 使用类别。 - -其次，在单机模型训练中对比了一下主要 API 的使用 - -| 名称 | PaddlePaddle | PyTorch | -| ------- | ----------------------------- | ------------------------------ | -| layer | nn.Layer | nn.Module | -| 各种层 | nn.layer2D(即 paddle 使用大写 D) | nn.layer2d(即 PyTorch 使用小写 d) | -| flatten | nn.Flatten | var.view(var.size(0), -1) | -| concat | paddle.concat | torch.cat | -| optim | paddle.optimizer | torch.optim | - -## 2、从具体 API 的参数设计差异作对比分析 - -通过训练和测试 Paddle 的动态图单机模型，就个人体验而言，对模型中常用到的一些 API 作简要分析，如 paddle.to_tensor，paddle.save,paddle.load,paddle.nn.Conv2D , paddle.nn.Linear, paddle.nn.CrossEntropyLoss , paddle.io.DataLoader - -### 2.1 基础操作类 API - -```python -#paddle.to_tensor -paddle.to_tensor(data, - dtype=None, - place=None, - stop_gradient=True) -#torch.tensor -torch.tensor(data, - dtype=None, - device=None, - requires_grad=False, - pin_memory=False) -``` - -在 paddle.to_tensor 中，stop_gradient 表示是否阻断梯度传导，PyTorch 的 requires_grad 表示是否不阻断梯度传导。 - -在 torch.tensor 中，pin_memeory 表示是否使用锁页内存，而 PaddlePaddle 却无此参数。 - ------- - -```python -#paddle.load -paddle.load(path, **configs) - -#torch.load -torch.load(f, - map_location=None, - pickle_module=pickle, - **pickle_load_args) -``` - -在 torch.load 中， pickle_module 表示用于 unpickling 元数据和对象的模块，PaddlePaddle 无此参数。 map_location 表示加载模型的位置，PaddlePaddle 无此参数。 - -在加载内容上，PyTorch 可以加载 torch.Tensor、torch.nn.Module、优化器等多个类型的数据。 -PaddlePaddle 只能加载 paddle.nn.Layer、优化器这两个类型的数据，这方面 PyTorch 更优一些。 - ------- - -```python -#paddle.save -paddle.save(obj, path, pickle_protocol=2) - -#torch.save -torch.save(obj, - f, - pickle_module=pickle, - pickle_protocol=2) -``` - -在 paddle.save 中， path 表示存储的路径，这一点比 PyTorch 的 f 更为清晰一些。 - -在 torch.save 中， pickle_module 表示用于 pickling 元数据和对象的模块，PaddlePaddle 无此参数。 - -还有在存储内容上，跟 paddle.load 情况类似，PaddlePaddle 只能存储 paddle.nn.Layer、优化器这两个类型的数据，个人觉得这方面 PaddlePaddle 有待加强。 - ------- - -### 2.2 组网类 API - -```python -#paddle.nn.Conv2D -paddle.nn.Conv2D(in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - padding_mode='zeros', - weight_attr=None, - bias_attr=None, - data_format='NCHW') -#torch.nn.Conv2d -torch.nn.Conv2d(in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True, - padding_mode='zeros') -``` - -在 paddle.nn.Conv2D 中，PaddlePaddle 支持 NCHW 和 NHWC 两种格式的输入（通过 data_format 设置）。而 PyTorch 只支持 NCHW 的输入，这一点 PaddlePaddle 更优一些。 - ------- - -```python -#paddle.nn.Linear -paddle.nn.Linear(in_features, out_features, weight_attr=None, bias_attr=None, name=None) -#torch.nn.Linear -torch.nn.Linear(in_features, out_features, bias=True) -``` - -在 paddle.nn.Linear 中，weight_attr/bias_attr 默认使用默认的权重/偏置参数属性，否则为指定的权重/偏置参数属性，而 PyTorch 的 bias 默认为 True，表示使用可更新的偏置参数。需要注意的是在 PaddlePaddle 中，当 bias_attr 设置为 bool 类型与 PyTorch 的作用一致。 - ------- - -### 2.3 Loss 类 API - -```python -#paddle.nn.CrossEntropyLoss -paddle.nn.CrossEntropyLoss(weight=None, - ignore_index=-100, - reduction='mean', - soft_label=False, - axis=-1, - use_softmax=True, - name=None) -#torch.nn.CrossEntropyLoss -torch.nn.CrossEntropyLoss(weight=None, - size_average=None, - ignore_index=-100, - reduce=None, - reduction='mean') -``` - -在 paddle.nn.CrossEntropyLoss 中， use_softmax 表示在使用交叉熵之前是否计算 softmax，PyTorch 无此参数；soft_label 指明 label 是否为软标签，PyTorch 无此参数；而 axis 表示进行 softmax 计算的维度索引，PyTorch 无此参数。 在这个 API 中，个人感觉 PaddlePaddle 的表现优于 PyTorch。 - ------- - -### 2.4 工具类 API - -```python -#paddle.io.DataLoader -paddle.io.DataLoader(dataset, - feed_list=None, - places=None, - return_list=False, - batch_sampler=None, - batch_size=1, - shuffle=False, - drop_last=False, - collate_fn=None, - num_workers=0, - use_buffer_reader=True, - use_shared_memory=False, - timeout=0, - worker_init_fn=None) -#torch.utils.data.DataLoader -torch.utils.data.DataLoader(dataset, - batch_size=1, - shuffle=False, - sampler=None, - batch_sampler=None, - num_workers=0, - collate_fn=None, - pin_memory=False, - drop_last=False, - timeout=0, - worker_init_fn=None, - multiprocessing_context=None, - generator=None, - prefetch_factor=2, - persistent_workers=False) -``` - -在 paddle.io.DataLoader 中， feed_list 表示 feed 变量列表，PyTorch 无此参数。 use_shared_memory 表示是否使用共享内存来提升子进程将数据放入进程间队列的速度，PyTorch 无此参数。 - -在 torch.utils.data.DataLoader 中，prefetch_factor 表示每个 worker 预先加载的数据数量，PaddlePaddle 无此参数；还有就是 PyTorch 可通过设置 sampler 自定义数据采集器，PaddlePaddle 只能自定义一个 DataLoader 来实现该功能，会有些繁琐。总的来说，这部分 PyTorch 的体验更好一些。 - ------- - -​ 从整体的 API 使用上，感觉 paddle 升级后的 paddle.xxx （例如：paddle.device paddle.nn paddle.vision ）比之前的 padddle.fluid.xxx 好用很多，还有就是新增加的高层 API 个人比较喜欢，一是对初学者比较友好、易用，二是对于开发者可以节省代码量，更简洁直观一些，在（六、动态图单机训练）中进行了代码展示和对比分析。 - -与 PyTorch 相比，基础 API 的结构和调用没有太大区别，但是在速度上，paddle 的基础 API 会更快一点，如果是利用了 paddle 高层 API，速度会快很多，在同样 5 次 epoch 的情况下，LeNet 训练高层 API 用 38s 左右,基础 API 得用将近两分钟，所以用高层 API 能减少大约三分之二的训练时间。 - -总体来说，使用像 paddle.Model、paddle.vision 这样的高级 API 进行封装调用，使用体验比较好，个人感觉在以后深度学习模型普遍使用时，高层 API 会更受欢迎，也会成为模型训练测试中更为流行的一种方法。 - - - -# 四、Tensor 索引 - -在了解 Paddle 的 Tensor 索引和其切片规则以及逻辑相关函数重写规则等内容后，结合指南内容（ https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/tensor_introduction_cn.html#id1 ）和模型训练过程中的 Tensor 索引使用，共有以下几点体验总结： - -**一、Paddle 可以使用静态数组索引；不可以使用 tensor 索引。** -示例：广播 (broadcasting) -1.每个张量至少为一维张量 - -2.从后往前比较张量的形状，当前维度的大小要么相等，要么其中一个等于一，要么其中一个不存在 - -```python -import paddle -x = paddle.ones((2, 3, 4)) -y = paddle.ones((2, 3, 4)) -# 两个张量 形状一致，可以广播 -z = x + y -print(z.shape) -# [2, 3, 4] - -x = paddle.ones((2, 3, 1, 5)) -y = paddle.ones((3, 4, 1)) -# 从后向前依次比较： -# 第一次：y 的维度大小是 1 -# 第二次：x 的维度大小是 1 -# 第三次：x 和 y 的维度大小相等 -# 第四次：y 的维度不存在 -# 所以 x 和 y 是可以广播的 -z = x + y -print(z.shape) -# [2, 3, 4, 5] - -# 相反 -x = paddle.ones((2, 3, 4)) -y = paddle.ones((2, 3, 6)) -# 此时 x 和 y 是不可广播的，因为第一次比较 4 不等于 6 -# z = x + y -# InvalidArgumentError: Broadcast dimension mismatch. -``` - -**二、两个张量进行广播语义后的结果张量的形状计算规则如下：** - -1.如果两个张量的形状的长度不一致，那么需要在较小形状长度的矩阵向前添加 1，直到两个张量的形状长度相等。 - -2.保证两个张量形状相等之后，每个维度上的结果维度就是当前维度上较大的那个。 - -```python -import paddle - -x = paddle.ones((2, 1, 4)) -y = paddle.ones((3, 1)) -z = x + y -print(z.shape) -# z 的形状: [2,3,4] - - -x = paddle.ones((2, 1, 4)) -y = paddle.ones((3, 2)) -# z = x + y -# ValueError: (InvalidArgument) Broadcast dimension mismatch. -``` - -**三、Paddle 目前支持的 Tensor 索引规则：** - -**Paddle 目前支持的 Tensor 索引状态：** - -1、基于 0-n 的下标进⾏索引 -2、如果下标为负数，则从尾部开始 -3、通过冒号 : 分隔切⽚参数 start:stop:step 来进⾏切⽚操作，其中 start、stop、step 均可缺省 - -示例 1：索引 - -```python -ndim_1_tensor = paddle.to_tensor([0, 1, 2, 3, 4, 5, 6, 7, 8]) -print("最初的 Tensor: ", ndim_1_tensor.numpy()) -print("取⾸端元素:", ndim_1_tensor[0].numpy()) -print("取末端元素:", ndim_1_tensor[-1].numpy()) -print("取所有元素:", ndim_1_tensor[:].numpy()) -print("取索引 3 之前的所有元素:", ndim_1_tensor[:3].numpy()) -print("取从索引 6 开始的所有元素:", ndim_1_tensor[6:].numpy()) -print("取从索引 3 开始到索引 6 之前的所有元素:", ndim_1_tensor[3:6].numpy()) -print("间隔 3 取所有元素:", ndim_1_tensor[::3].numpy()) -print("逆序取所有元素:", ndim_1_tensor[::-1].numpy()) -``` - -部分运⾏结果如下： - -```python -First element: [0] -Last element: [8] -All element: [0 1 2 3 4 5 6 7 8] -Before 3: [0 1 2] -From 6 to the end: [6 7 8] -From 3 to 6: [3 4 5] -Interval of 3: [0 3 6] -Reverse: [8 7 6 5 4 3 2 1 0] -``` - -**Paddle 目前不支持的 Tensor 索引状态：** - -示例 1：不能维度直接赋值 - -```python -#报错： -TypeError: 'paddle.fluid.libpaddle.VarBase' object does not support item assignment -#代码如下： -# pytorch code -Pred_boxes[:, 0] = pred_ctr_x - 0.5 * pred_w -pred_boxes[:, 1] = pred_ctr_y - 0.5 * pred_h -pred_boxes[:, 2] = pred_ctr_x + 0.5 * pred_w -pred_boxes[:, 3] = pred_ctr_y + 0.5 * pred_h - -# paddlepaddle code -pred_boxes = paddle.layers.concat([ - pred_ctr_x - 0.5 * pred_w, - pred_ctr_y - 0.5 * pred_h, - pred_ctr_x + 0.5 * pred_w, - pred_ctr_y + 0.5 * pred_h -]) - - -#维度报错: - -too many indices (3) for tensor of dimension 2 -#代码如下： -# pytorch code -bbox_x[bind, :, np.newaxis ] -# paddlepaddle code -paddle.layers.reshape(bbox_x[bind, :], [1, -1, 1]) -``` - -示例 2： tensor 的值不能直接利用 - -报错：paddlepaddle 中的 value 不能直接拿出来用。 - -```python -TypeError: The type of 'shape' in reshape must be list[int] or tuple(int) in - Dygraph mode, but received , which contains Variable. -#错误代码：其中 stack_size, feat_size 为 tensor。 - -#代码如下： -# paddlepaddle code -shift_x1 = paddle.layers.reshape(paddle.dygraph.to_variable(shift_x1), [1, stack_size, feat_size[1]]) - -``` - -改进加入 - -```python -# paddlepaddle code -stack_size = stack_size.numpy() -feat_size = feat_size.numpy() -``` - -**四、Tensor 索引整体体验** - -感觉在通过索引或切片修改 Tensor 的整体过程有些冗余，稳定性也会下降。虽然使用指南里说明了修改会导致原值不会被保存，可能会给梯度计算引入风险 ，但是在这点上个人感觉 PyTorch 的体验要好于 Paddle。 - -总的来说，在模型训练中利用 Tensor 加载数据集等操作上 PyTorch 与 Paddle 的体验并没有太大区别，但整体的感觉 PyTorch 的 Tensor 索引更好一些，个人感觉 Paddle 在修改 Tensor 的部分上可以增加一些文档说明。 - -**文档序号错误小提醒：** - - https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/update_cn.html#tensor 中的“使用 Tensor 概念表示数据”下的序号应为 1、2、；文档中为两个 1、1、。 - -# 五、NumPy 兼容性分析及对比 - -NumPy 在 Paddle 的体验，感觉和 PyTorch 的体验并无区别，但是在阅读使用文档时的体验感较好，内容叙述很详细 （文档链接：https://www.paddlepaddle.org.cn/tutorials/projectdetail/3466356 ） - -**1、关于 numpy API 的重写** - -在 Paddle 动态图单机训练中，所有与组网相关的 numpy 操作都必须用 paddle 的 API 重新实现 ，这一点个人认为需要注意，因为在习惯使用 PyTorch 代码逻辑时，转为 PaddlePaddle 容易出错，下面举例说明： - -```python -#下述样例需要将 forward 中的所有的 numpy 操作都转为 Paddle API： -def forward(self, x): - out = self.linear(x) # [bs, 3] - - # 以下将 tensor 转为了 numpy 进行一系列操作 - x_data = x.numpy().astype('float32') # [bs, 10] - weight = np.random.randn([10,3]) - mask = paddle.to_tensor(x_data * weight) # 此处又转回了 Tensor - - out = out * mask - return out -``` - -注：由于在动态图模型代码中的 numpy 相关的操作不可以转为静态图，所以在动态图单机训练时候，只要与组网相关的 numpy 操作用 paddle 的 API 重新实现即可，所以在 numpy API 的重写部分，记住以上区别可以防止 Segment Fault 等错误的产生。 - -**2、关于 Tensor 操作的支持** - -在动态图单机训练中，感觉 Paddle 的 Tensor 高度兼容 Numpy 数组（array），发现增加了很多适用于深度学习任务的参数和方法，如反向计算梯度，更灵活的指定运行硬件 ，还有就是 Paddle 的 Tensor 可以与 Numpy 的数组方便的互转 ，比如以下代码展示： - -```python -import paddle -import numpy as np - -tensor_to_convert = paddle.to_tensor([1.,2.]) - -#通过 Tensor.numpy() 方法，将 Tensor 转化为 Numpy 数组 -tensor_to_convert.numpy() - -#通过 paddle.to_tensor() 方法，将 Numpy 数组 转化为 Tensor -tensor_temp = paddle.to_tensor(np.array([1.0, 2.0])) -``` - -**3、numpy 与 tensor 的转换补充** - -numpy 操作多样, 简单. 但网络前向只能是 tensor 类型, 各有优势, 所以需要相互转换补充. - -```python -# convert Tensor x of torch to array y of numpy: -y = x.numpy(); - -# convert array x of numpy to Tensor y of torch: -y = torch.from_numpy(x) - -# 先将数据转换成 Tensor, 再使用 CUDA 函数来将 Tensor 移动到 GPU 上加速 -如果想把 CUDA tensor 格式的数据改成 numpy 时，需要先将其转换成 cpu float-tensor 随后再转到 numpy 格式。 -x_np = x.data.numpy() - -# 改为： - -x_np = x.data.cpu().numpy() - -# 或者兼容上面两者的方式 -x_np = x.detach().cpu().numpy() if x.requires_grad else x.cpu().numpy() - -``` - -**整体体验：** - -感觉对于刚使用 Paddle 的新手，这部分需要注意的就是 Paddle 的 Tensor 虽然可以与 Numpy 的数组方便的互相转换 ，但是有两个场景优先使用 Paddle 的 Tensor 比较好: - -- 场景一：在组网程序中，对网络中向量的处理，务必使用 Tensor，而不建议转成 Numpy 的数组。如果在组网过程中转成 Numpy 的数组，并使用 Numpy 的函数会拖慢整体性能； -- 场景二：在数据处理和模型后处理等场景，建议优先使用 Tensor，主要是飞桨为 AI 硬件做了大量的适配和性能优化工作，部分情况下会获得更好的使用体验和性能。 - -建议：这两个场景内容可以增加一些实例，可能会使新手在这部分的理解更为透彻。 - -总体来说：Tensor 与 Numpy 数组的兼容与转换，Paddle 体验更好一点，兼容性上与 PyTorch 感觉没区别，但是 Paddle 的兼容转换处理上更具有一些前瞻性。 - -# 六、动态图单机训练 - -（1）使用 PyTorch 完成一个图像分类的动态图单机训练例子（MNIST 数据集） - -```python -import torch -from torch import nn -from net import LeNet -from torch.optim import lr_scheduler -from torchvision import datasets, transforms - -data_transform = transforms.Compose([ - transforms.ToTensor() # 仅对数据做转换为 tensor 格式操作 -]) - -# 加载训练数据集 -train_dataset = datasets.MNIST(root='./data', train=True, transform=data_transform, download=True) -# 给训练集创建一个数据集加载器 -train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=16, shuffle=True) -# 加载测试数据集 -test_dataset = datasets.MNIST(root='./data', train=False, transform=data_transform, download=True) -# 给测试集创建一个数据集加载器 -test_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=16, shuffle=True) - -# 如果显卡可用，则用显卡进行训练 -device = "cuda" if torch.cuda.is_available() else 'cpu' - -# 调用 net 里定义的模型，如果 GPU 可用则将模型转到 GPU -model = LeNet().to(device) - -# 定义损失函数（交叉熵损失） -loss_fn = nn.CrossEntropyLoss() - -# 定义优化器（SGD：随机梯度下降） -optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9) - -# 学习率每隔 10 个 epoch 变为原来的 0.1 -lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) - -# 定义训练函数 -def train(dataloader, model, loss_fn, optimizer): - loss, current, n = 0.0, 0.0, 0 - for batch, (X, y) in enumerate(dataloader): - # 前向传播 - X, y = X.to(device), y.to(device) - output = model(X) - cur_loss = loss_fn(output, y) - _, pred = torch.max(output, axis=1) - cur_acc = torch.sum(y == pred) / output.shape[0] - # 反向传播 - optimizer.zero_grad() - cur_loss.backward() - optimizer.step() - loss += cur_loss.item() - current += cur_acc.item() - n = n + 1 - print('train_loss：' + str(loss / n)) - print('train_acc：' + str(current / n)) - -# 定义测试函数 -def test(dataloader, model, loss_fn): - # 将模型转换为验证模式 - model.eval() - loss, current, n = 0.0, 0.0, 0 - # 非训练，推理期用到（测试时模型参数不用更新，所以 no_grad） - with torch.no_grad(): - for batch, (X, y) in enumerate(dataloader): - X, y = X.to(device), y.to(device) - output = model(X) - cur_loss = loss_fn(output, y) - _, pred = torch.max(output, axis=1) - cur_acc = torch.sum(y == pred) / output.shape[0] - loss += cur_loss.item() - current += cur_acc.item() - n = n + 1 - print('test_loss：' + str(loss / n)) - print('test_acc：' + str(current / n)) - -# 开始训练 -epoch = 5 -for t in range(epoch): - lr_scheduler.step() - print(f"Epoch {t + 1}\n----------------------") - train(train_dataloader, model, loss_fn, optimizer) - test(test_dataloader, model, loss_fn) - torch.save(model.state_dict(), "save_model/{}model.pth".format(t)) # 模型保存 -print("Done!") -``` - -（2）使用 Paddle 完成一个图像分类的动态图单机训练例子（MNIST 数据集） - -```python -import paddle -from paddle.vision.transforms import Compose, Normalize -transform = Compose([Normalize(mean=[127.5], - std=[127.5], - data_format='CHW')]) -# 使用 transform 对数据集做归一化 -print('download training data and load training data') -train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform) -test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform) -print('load finished') - -import paddle -import paddle.nn.functional as F -class LeNet(paddle.nn.Layer): - def __init__(self): - super(LeNet, self).__init__() - self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2) - self.max_pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2) - self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1) - self.max_pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2) - self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120) - self.linear2 = paddle.nn.Linear(in_features=120, out_features=84) - self.linear3 = paddle.nn.Linear(in_features=84, out_features=10) - - def forward(self, x): - x = self.conv1(x) - x = F.relu(x) - x = self.max_pool1(x) - x = self.conv2(x) - x = F.relu(x) - x = self.max_pool2(x) - x = paddle.flatten(x, start_axis=1,stop_axis=-1) - x = self.linear1(x) - x = F.relu(x) - x = self.linear2(x) - x = F.relu(x) - x = self.linear3(x) - return x - -#方法一 高层 API -from paddle.metric import Accuracy -model = paddle.Model(LeNet()) # 用 Model 封装模型 -optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) - -# 配置模型 -model.prepare( - optim, - paddle.nn.CrossEntropyLoss(), - Accuracy() - ) - -# 训练模型 -model.fit(train_dataset, - epochs=5, - batch_size=64, - verbose=1 - ) -model.evaluate(test_dataset, batch_size=64, verbose=1) - -#方法 2 基础 API -import paddle.nn.functional as F -train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True) -# 加载训练集 batch_size 设为 64 -def train(model): - model.train() - epochs = 2 - optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters()) - # 用 Adam 作为优化函数 - for epoch in range(epochs): - for batch_id, data in enumerate(train_loader()): - x_data = data[0] - y_data = data[1] - predicts = model(x_data) - loss = F.cross_entropy(predicts, y_data) - # 计算损失 - acc = paddle.metric.accuracy(predicts, y_data) - loss.backward() - if batch_id % 300 == 0: - print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy())) - optim.step() - optim.clear_grad() -model = LeNet() -train(model) - -test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64) -# 加载测试数据集 -def test(model): - model.eval() - batch_size = 64 - for batch_id, data in enumerate(test_loader()): - x_data = data[0] - y_data = data[1] - predicts = model(x_data) - # 获取预测结果 - loss = F.cross_entropy(predicts, y_data) - acc = paddle.metric.accuracy(predicts, y_data) - if batch_id % 20 == 0: - print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, loss.numpy(), acc.numpy())) -test(model) -``` - -（3）两个程序的运行结果 - -一、PyTorch 程序运行结果 - -```python -#下载数据 -Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz -Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data\MNIST\raw\train-images-idx3-ubyte.gz -9913344it [00:03, 2813467.92it/s] -Extracting ./data\MNIST\raw\train-images-idx3-ubyte.gz to ./data\MNIST\raw - -Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz -Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data\MNIST\raw\train-labels-idx1-ubyte.gz -29696it [00:00, 29740700.00it/s] -Extracting ./data\MNIST\raw\train-labels-idx1-ubyte.gz to ./data\MNIST\raw - -Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz -Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data\MNIST\raw\t10k-images-idx3-ubyte.gz -1649664it [00:01, 1119159.12it/s] -Extracting ./data\MNIST\raw\t10k-images-idx3-ubyte.gz to ./data\MNIST\raw - -Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz -Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data\MNIST\raw\t10k-labels-idx1-ubyte.gz -5120it [00:00, 5302428.76it/s] -Extracting ./data\MNIST\raw\t10k-labels-idx1-ubyte.gz to ./data\MNIST\raw -``` - -```python -#五次 epoch 结果 -Epoch 1 ----------------------- -train_loss：2.301828587023417 -train_acc：0.10976666666666667 -test_loss：2.2998157671610513 -test_acc：0.186 -Epoch 2 ----------------------- -train_loss：2.292567366727193 -train_acc：0.13415 -test_loss：2.268421948369344 -test_acc：0.20193333333333333 -Epoch 3 ----------------------- -train_loss：1.2924396817684174 -train_acc：0.5939 -test_loss：0.5503138323009014 -test_acc：0.82955 -Epoch 4 ----------------------- -train_loss：0.45181470778187116 -train_acc：0.86275 -test_loss：0.3795674962008993 -test_acc：0.88765 -Epoch 5 ----------------------- -train_loss：0.35491258655836183 -train_acc：0.8926666666666667 -test_loss：0.3223567478398482 -test_acc：0.9044666666666666 -``` - -二、Paddle 程序运行结果 - -由于 paddle 文档中提供的数据集下载代码一直报错(已在报错汇总中展示)，故进行了手动下载数据集 - -​ 1、使用高层 API 结果 - -```python -#附第 5 个 epoch -Epoch 5/5 -step 10/938 [..............................] - loss: 0.0325 - acc: 0.9938 - ETA: 35s - 38ms/step -step 20/938 [..............................] - loss: 0.0050 - acc: 0.9922 - ETA: 32s - 35ms/step -step 30/938 [..............................] - loss: 0.0094 - acc: 0.9932 - ETA: 29s - 32ms/step -step 40/938 [>.............................] - loss: 0.0344 - acc: 0.9910 - ETA: 27s - 31ms/step -step 50/938 [>.............................] - loss: 0.0020 - acc: 0.9916 - ETA: 26s - 30ms/step -step 60/938 [>.............................] - loss: 0.0121 - acc: 0.9917 - ETA: 25s - 29ms/step -step 70/938 [=>............................] - loss: 0.0026 - acc: 0.9913 - ETA: 25s - 29ms/step -step 80/938 [=>............................] - loss: 0.0151 - acc: 0.9914 - ETA: 24s - 29ms/step -step 90/938 [=>............................] - loss: 0.0030 - acc: 0.9910 - ETA: 24s - 28ms/step -step 100/938 [==>...........................] - loss: 0.1395 - acc: 0.9905 - ETA: 23s - 28ms/step -step 110/938 [==>...........................] - loss: 0.0344 - acc: 0.9902 - ETA: 23s - 28ms/step -step 120/938 [==>...........................] - loss: 0.0175 - acc: 0.9904 - ETA: 23s - 28ms/step -step 130/938 [===>..........................] - loss: 0.0511 - acc: 0.9899 - ETA: 22s - 28ms/step -step 140/938 [===>..........................] - loss: 0.0136 - acc: 0.9903 - ETA: 22s - 28ms/step -step 150/938 [===>..........................] - loss: 0.0068 - acc: 0.9901 - ETA: 21s - 28ms/step -step 160/938 [====>.........................] - loss: 0.0128 - acc: 0.9898 - ETA: 21s - 28ms/step -step 170/938 [====>.........................] - loss: 0.0447 - acc: 0.9898 - ETA: 21s - 28ms/step -step 180/938 [====>.........................] - loss: 0.0275 - acc: 0.9900 - ETA: 20s - 28ms/step -step 190/938 [=====>........................] - loss: 0.0488 - acc: 0.9901 - ETA: 20s - 28ms/step -step 200/938 [=====>........................] - loss: 0.0593 - acc: 0.9899 - ETA: 20s - 28ms/step -step 210/938 [=====>........................] - loss: 0.0049 - acc: 0.9899 - ETA: 20s - 28ms/step -step 220/938 [======>.......................] - loss: 0.0186 - acc: 0.9898 - ETA: 19s - 27ms/step -step 230/938 [======>.......................] - loss: 0.0214 - acc: 0.9900 - ETA: 19s - 27ms/step -step 240/938 [======>.......................] - loss: 0.0067 - acc: 0.9902 - ETA: 19s - 27ms/step -step 250/938 [======>.......................] - loss: 0.0195 - acc: 0.9902 - ETA: 18s - 27ms/step -step 260/938 [=======>......................] - loss: 0.0310 - acc: 0.9901 - ETA: 18s - 27ms/step -step 270/938 [=======>......................] - loss: 0.0248 - acc: 0.9902 - ETA: 18s - 27ms/step -step 280/938 [=======>......................] - loss: 0.0213 - acc: 0.9901 - ETA: 17s - 27ms/step -step 290/938 [========>.....................] - loss: 0.0156 - acc: 0.9903 - ETA: 17s - 27ms/step -step 300/938 [========>.....................] - loss: 0.0069 - acc: 0.9906 - ETA: 17s - 27ms/step -step 310/938 [========>.....................] - loss: 0.0361 - acc: 0.9904 - ETA: 17s - 27ms/step -step 320/938 [=========>....................] - loss: 0.0418 - acc: 0.9904 - ETA: 16s - 27ms/step -step 330/938 [=========>....................] - loss: 0.0060 - acc: 0.9903 - ETA: 16s - 27ms/step -step 340/938 [=========>....................] - loss: 0.0587 - acc: 0.9903 - ETA: 16s - 27ms/step -step 350/938 [==========>...................] - loss: 0.0434 - acc: 0.9904 - ETA: 16s - 27ms/step -step 360/938 [==========>...................] - loss: 6.9384e-04 - acc: 0.9904 - ETA: 15s - 27ms/step -step 370/938 [==========>...................] - loss: 0.0134 - acc: 0.9904 - ETA: 15s - 27ms/step -step 380/938 [===========>..................] - loss: 0.0278 - acc: 0.9903 - ETA: 15s - 27ms/step -step 390/938 [===========>..................] - loss: 5.5189e-04 - acc: 0.9902 - ETA: 14s - 27ms/step -step 400/938 [===========>..................] - loss: 0.0023 - acc: 0.9904 - ETA: 14s - 27ms/step -step 410/938 [============>.................] - loss: 0.0105 - acc: 0.9904 - ETA: 14s - 27ms/step -step 420/938 [============>.................] - loss: 0.0398 - acc: 0.9901 - ETA: 14s - 27ms/step -step 430/938 [============>.................] - loss: 0.0169 - acc: 0.9902 - ETA: 13s - 27ms/step -step 440/938 [=============>................] - loss: 0.0013 - acc: 0.9902 - ETA: 13s - 27ms/step -step 450/938 [=============>................] - loss: 0.0074 - acc: 0.9901 - ETA: 13s - 27ms/step -step 460/938 [=============>................] - loss: 0.0651 - acc: 0.9899 - ETA: 12s - 27ms/step -step 470/938 [==============>...............] - loss: 0.0130 - acc: 0.9900 - ETA: 12s - 27ms/step -step 480/938 [==============>...............] - loss: 0.0677 - acc: 0.9900 - ETA: 12s - 27ms/step -step 490/938 [==============>...............] - loss: 0.0147 - acc: 0.9901 - ETA: 12s - 27ms/step -step 500/938 [==============>...............] - loss: 0.0120 - acc: 0.9901 - ETA: 11s - 27ms/step -step 510/938 [===============>..............] - loss: 0.0191 - acc: 0.9901 - ETA: 11s - 27ms/step -step 520/938 [===============>..............] - loss: 0.0296 - acc: 0.9901 - ETA: 11s - 27ms/step -step 530/938 [===============>..............] - loss: 0.0488 - acc: 0.9900 - ETA: 11s - 27ms/step -step 540/938 [================>.............] - loss: 0.0239 - acc: 0.9901 - ETA: 10s - 27ms/step -step 550/938 [================>.............] - loss: 0.0303 - acc: 0.9900 - ETA: 10s - 27ms/step -step 560/938 [================>.............] - loss: 0.0287 - acc: 0.9900 - ETA: 10s - 27ms/step -step 570/938 [=================>............] - loss: 0.0375 - acc: 0.9900 - ETA: 9s - 27ms/step -step 580/938 [=================>............] - loss: 0.0197 - acc: 0.9900 - ETA: 9s - 27ms/step -step 590/938 [=================>............] - loss: 0.0265 - acc: 0.9900 - ETA: 9s - 27ms/step -step 600/938 [==================>...........] - loss: 0.0615 - acc: 0.9901 - ETA: 9s - 27ms/step -step 610/938 [==================>...........] - loss: 0.0036 - acc: 0.9901 - ETA: 8s - 27ms/step -step 620/938 [==================>...........] - loss: 0.0079 - acc: 0.9900 - ETA: 8s - 27ms/step -step 630/938 [===================>..........] - loss: 0.0071 - acc: 0.9901 - ETA: 8s - 27ms/step -step 640/938 [===================>..........] - loss: 6.9407e-04 - acc: 0.9902 - ETA: 8s - 27ms/step -step 650/938 [===================>..........] - loss: 0.0024 - acc: 0.9902 - ETA: 7s - 27ms/step -step 660/938 [====================>.........] - loss: 0.0016 - acc: 0.9902 - ETA: 7s - 27ms/step -step 670/938 [====================>.........] - loss: 0.0069 - acc: 0.9901 - ETA: 7s - 27ms/step -step 680/938 [====================>.........] - loss: 0.0023 - acc: 0.9901 - ETA: 6s - 27ms/step -step 690/938 [=====================>........] - loss: 0.0089 - acc: 0.9901 - ETA: 6s - 27ms/step -step 700/938 [=====================>........] - loss: 0.0108 - acc: 0.9900 - ETA: 6s - 27ms/step -step 710/938 [=====================>........] - loss: 0.0155 - acc: 0.9899 - ETA: 6s - 27ms/step -step 720/938 [======================>.......] - loss: 0.0303 - acc: 0.9898 - ETA: 5s - 27ms/step -step 730/938 [======================>.......] - loss: 0.0405 - acc: 0.9898 - ETA: 5s - 27ms/step -step 740/938 [======================>.......] - loss: 0.0304 - acc: 0.9899 - ETA: 5s - 27ms/step -step 750/938 [======================>.......] - loss: 0.0065 - acc: 0.9897 - ETA: 5s - 27ms/step -step 760/938 [=======================>......] - loss: 0.0091 - acc: 0.9898 - ETA: 4s - 27ms/step -step 770/938 [=======================>......] - loss: 0.0371 - acc: 0.9896 - ETA: 4s - 27ms/step -step 780/938 [=======================>......] - loss: 0.0048 - acc: 0.9896 - ETA: 4s - 27ms/step -step 790/938 [========================>.....] - loss: 0.0036 - acc: 0.9897 - ETA: 4s - 27ms/step -step 800/938 [========================>.....] - loss: 0.0233 - acc: 0.9896 - ETA: 3s - 27ms/step -step 810/938 [========================>.....] - loss: 0.0547 - acc: 0.9896 - ETA: 3s - 27ms/step -step 820/938 [=========================>....] - loss: 0.0011 - acc: 0.9896 - ETA: 3s - 27ms/step -step 830/938 [=========================>....] - loss: 0.0079 - acc: 0.9896 - ETA: 2s - 27ms/step -step 840/938 [=========================>....] - loss: 0.0132 - acc: 0.9896 - ETA: 2s - 27ms/step -step 850/938 [==========================>...] - loss: 0.0134 - acc: 0.9896 - ETA: 2s - 27ms/step -step 860/938 [==========================>...] - loss: 0.0065 - acc: 0.9896 - ETA: 2s - 27ms/step -step 870/938 [==========================>...] - loss: 0.0106 - acc: 0.9897 - ETA: 1s - 27ms/step -step 880/938 [===========================>..] - loss: 0.0312 - acc: 0.9896 - ETA: 1s - 27ms/step -step 890/938 [===========================>..] - loss: 0.0169 - acc: 0.9897 - ETA: 1s - 27ms/step -step 900/938 [===========================>..] - loss: 0.0187 - acc: 0.9897 - ETA: 1s - 27ms/step -step 910/938 [============================>.] - loss: 0.0925 - acc: 0.9897 - ETA: 0s - 27ms/step -step 920/938 [============================>.] - loss: 0.0317 - acc: 0.9898 - ETA: 0s - 27ms/step -step 930/938 [============================>.] - loss: 0.0448 - acc: 0.9898 - ETA: 0s - 27ms/step -step 938/938 [==============================] - loss: 0.0140 - acc: 0.9897 - 27ms/step -Eval begin... -step 10/157 [>.............................] - loss: 0.2273 - acc: 0.9828 - ETA: 1s - 12ms/step -step 20/157 [==>...........................] - loss: 0.1525 - acc: 0.9773 - ETA: 1s - 11ms/step -step 30/157 [====>.........................] - loss: 0.1391 - acc: 0.9771 - ETA: 1s - 11ms/step -step 40/157 [======>.......................] - loss: 0.0088 - acc: 0.9785 - ETA: 1s - 11ms/step -step 50/157 [========>.....................] - loss: 0.0051 - acc: 0.9803 - ETA: 1s - 11ms/step -step 60/157 [==========>...................] - loss: 0.1621 - acc: 0.9797 - ETA: 1s - 10ms/step -step 70/157 [============>.................] - loss: 0.0265 - acc: 0.9795 - ETA: 0s - 10ms/step -step 80/157 [==============>...............] - loss: 0.0019 - acc: 0.9801 - ETA: 0s - 10ms/step -step 90/157 [================>.............] - loss: 0.0439 - acc: 0.9814 - ETA: 0s - 10ms/step -step 100/157 [==================>...........] - loss: 0.0033 - acc: 0.9828 - ETA: 0s - 10ms/step -step 110/157 [====================>.........] - loss: 3.9403e-04 - acc: 0.9837 - ETA: 0s - 10ms/step -step 120/157 [=====================>........] - loss: 6.5309e-04 - acc: 0.9846 - ETA: 0s - 10ms/step -step 130/157 [=======================>......] - loss: 0.0735 - acc: 0.9849 - ETA: 0s - 10ms/step -step 140/157 [=========================>....] - loss: 9.8257e-05 - acc: 0.9856 - ETA: 0s - 10ms/step -step 150/157 [===========================>..] - loss: 0.0412 - acc: 0.9859 - ETA: 0s - 10ms/step -step 157/157 [==============================] - loss: 2.9252e-04 - acc: 0.9860 - 10ms/step -Eval samples: 10000 -``` - -​ 2、使用基础 API 结果 - -```python -#附 5 次 epoch -epoch: 0, batch_id: 0, loss is: [2.9994564], acc is: [0.0625] -epoch: 0, batch_id: 300, loss is: [0.08384503], acc is: [0.96875] -epoch: 0, batch_id: 600, loss is: [0.06951822], acc is: [0.984375] -epoch: 0, batch_id: 900, loss is: [0.1054411], acc is: [0.953125] -epoch: 1, batch_id: 0, loss is: [0.0715376], acc is: [0.96875] -epoch: 1, batch_id: 300, loss is: [0.14129372], acc is: [0.953125] -epoch: 1, batch_id: 600, loss is: [0.00361754], acc is: [1.] -epoch: 1, batch_id: 900, loss is: [0.00827341], acc is: [1.] -epoch: 2, batch_id: 0, loss is: [0.05238173], acc is: [0.984375] -epoch: 2, batch_id: 300, loss is: [0.00865405], acc is: [1.] -epoch: 2, batch_id: 600, loss is: [0.03549637], acc is: [0.984375] -epoch: 2, batch_id: 900, loss is: [0.02600437], acc is: [1.] -epoch: 3, batch_id: 0, loss is: [0.02365134], acc is: [1.] -epoch: 3, batch_id: 300, loss is: [0.0848916], acc is: [0.953125] -epoch: 3, batch_id: 600, loss is: [0.01307216], acc is: [1.] -epoch: 3, batch_id: 900, loss is: [0.01843782], acc is: [1.] -epoch: 4, batch_id: 0, loss is: [0.00281677], acc is: [1.] -epoch: 4, batch_id: 300, loss is: [0.01466173], acc is: [1.] -epoch: 4, batch_id: 600, loss is: [0.04725911], acc is: [0.984375] -epoch: 4, batch_id: 900, loss is: [0.00772327], acc is: [1.] -batch_id: 0, loss is: [0.03467739], acc is: [0.984375] -batch_id: 20, loss is: [0.15250863], acc is: [0.953125] -batch_id: 40, loss is: [0.13340972], acc is: [0.984375] -batch_id: 60, loss is: [0.06206714], acc is: [0.953125] -batch_id: 80, loss is: [0.00384411], acc is: [1.] -batch_id: 100, loss is: [0.00386263], acc is: [1.] -batch_id: 120, loss is: [0.00981056], acc is: [1.] -batch_id: 140, loss is: [0.07646853], acc is: [0.984375] - -Process finished with exit code 0 -``` - -这部分简单说就是 Paddle 的高层 API 比基础 API 运行速度快，且简单好用，体验感较好。 - -与 PyTorch 相比，Paddle 文档中提供的代码下载不了数据集，需要手动下载。 - -# 七、各种 trick 的用法 - -PaddlePaddle 有丰富的 api 可以实现各种调参 trick，像 dropout，batchnormalization，groupnormalization，l2regularization, lr decay 等等都可以很轻松地实现。 -另外数据增强则推荐使用 PIL 库，尝试各种技巧不一定每次都能让模型准确度提升，毕竟训练神经网络是一个多参数配合的过程，只有练得多了才更容易找到最佳的方向。 -根据查阅资料，现总结以下几点： -1、 cuDNN 操作的选择 -在 use_cudnn=True 时，框架底层调用的是 cuDNN 中的卷积操作。 -通常 cuDNN 库提供的操作具有很好的性能表现，其性能明显优于 Paddle 原生的 CUDA 实现，比如 conv2d 。 -但是 cuDNN 中有些操作的性能较差，比如： conv2d_transpose 在 batch_size=1 时、pool2d 在 global_pooling=True 时等， -这些情况下，cuDNN 实现的性能差于 Paddle 的 CUDA 实现，建议手动设置 use_cudnn=False 。 - -2、使用融合功能的 API -用户网络配置中使用融合功能的 API，通常能取得更好的计算性能。 -例如 softmax_with_cross_entropy 通常会比 softmax cross_entropy 分开用好 - -3、优化数据准备速度的方法 -为降低训练的整体时间，建议用户使用异步数据读取的方式，并开启 use_double_buffer（默认开）。此外，用户可根据模型的实际情况设置数据队列的大小（capacity）。 -如果数据准备的时间大于模型执行的时间，或者出现了数据队列为空的情况，这时候需要考虑对 Python 的用户 reader 进行加速。常用的方法为：使用 Python 多进程准备数据。 -Python 端的数据预处理，都是使用 CPU 完成。如果 Paddle 提供了相应功能的 API，可将这部分预处理功能写到模型配置中，如此 Paddle 就可以使用 GPU 来完成该预处理功能， -这样也可以减轻 CPU 预处理数据的负担，提升总体训练速度。 - -4、显存优化策略 -GC（Garbage Collection）的原理是在网络运行阶段及时释放无用变量的显存空间，达到节省显存的目的。GC 适用于使用 Executor，ParallelExecutor 做模型训练/预测的场合。 -由于原生的 CUDA 系统调用 cudaMalloc 和 cudaFree 均是同步操作，非常耗时。因此与许多框架类似，PaddlePaddle 采用了显存预分配的策略加速显存分配。 - -5、Inplace 策略 -原理是 Op 的输出复用 Op 输入的显存空间。 -由于目前设计上的一些问题，在开启 Inplace 策略后，必须保证后续 exe.run 中 fetch_list 的变量是 persistable 的。 -fetch_list：结果获取表，训练时一般有 loss 等。 -推荐的最佳显存优化策略为： -开启 Inplace 策略：设置 build_strategy.enable_inplace = True ，并设置 fetch_list 中的 var.persistable = True 。 - -PaddlePaddle 在深度学习框架方面，已经覆盖了搜索、图像识别、语音语义识别理解、情感分析、机器翻译、用户画像推荐等多领域的业务和技术。 -基于动态图实现的 AlexNet 代码如下: - -```python -class ConvPoolLayer(nn.Layer): - '''卷积+池化''' - def __init__(self, - input_channels, - output_channels, - filter_size, - stride, - padding, - stdv, - groups=1, - act=None, - name=None): - super(ConvPoolLayer, self).__init__() - self.relu = ReLU() if act == "relu" else None - - self._conv = Conv2D(#返回一个由所有子层组成的列表。 - in_channels=input_channels, - out_channels=output_channels, - kernel_size=filter_size, - stride=stride, - padding=padding, - groups=groups, - weight_attr=ParamAttr( - name=name + "_weights", initializer=Uniform(-stdv, stdv)), - bias_attr=ParamAttr( - name=name + "_offset", initializer=Uniform(-stdv, stdv))) - self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0) - - def forward(self, inputs): - x = self._conv(inputs) - if self.relu is not None: - x = self.relu(x) - x = self._pool(x) - return x -``` - -PaddlePaddle 搭建 cnn 网络以及进行模型训练预测，可以说 PaddlePaddle 搭建训练 pipeline 还是比较方便的。 - -# 八、报错汇总 - -1、 DataLoader 报错问题 ： - -```python -SystemError: (Fatal) Blocking queue is killed because the data reader raises an exception. -[Hint: Expected killed_ != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158) -``` - -原因分析：由于 PaddlePaddle 和 PyTorch 两个框架在这部分并无区别，Paddle 读取数据在这主要用到两个类：paddle.io.Dataset 和 paddle.io.DataLoader，所以查看源代码后发现在 Dataset 类中的__getitem__(self, idx)返回的数据不是 numpy.ndarray 类型 - -解决方案： - - 在__getitem__（）函数里添加一行代码：image = paddle.to_tensor(image) - -```python -# define a random dataset -class RandomDataset(Dataset): - def __init__(self, num_samples): - self.num_samples = num_samples - - def __getitem__(self, idx): - image = np.random.random([784]).astype('float32') - label = np.random.randint(0, 9, (1, )).astype('int64') - image = paddle.to_tensor(image) # 添加这行代码 - return image, label - - def __len__(self): - return self.num_samples -``` - -注：还有一种情况是 Dataset 类的__getitem__(self, idx)返回的数据为字典（Dict） 类型也会报同样的错误，这时可把 return 改为 return {'input': image, 'lb': label} - - - -2、使用自己数据集的图像大小不合适而报错 - -```python -ERROR:root:DataLoader reader thread raised an exception! -Traceback (most recent call last): -File “/home/disk0/zw/workspace/PaddleOCR/test/load_data.py”, line 38, in -for idx,batch in enumerate(data_loader): -File “/home/disk0/wy/anaconda3/envs/paddle/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py”, line 788, in next -data = self.reader.read_next_var_list() -SystemError: (Fatal) Blocking queue is killed because the data reader raises an exception. -[Hint: Expected killed != true, but received killed_:1 == true:1.] (at /paddle/paddle/fluid/operators/reader/blocking_queue.h:158) -``` - -解决方案：由于自己数据集中有部分图片超过了默认 shape 的[3, 32, 320]，图片宽度大于了 320，所以直接删除或调大 shape 尺寸即可 - -注:使用公开数据集时不会出现此问题 - - - -3、使用 paddle.reshape 时出现错误 - -```python -ValueError: (InvalidArgument) The 'shape' in ReshapeOp is invalid. The input tensor X'size must be equal to the capacity of 'shape'. But received X's shape = [64, 50, 4, 4], X's size = 51200, 'shape' is [1, 800], the capacity of 'shape' is 800. - [Hint: Expected capacity == in_size, but received capacity:800 != in_size:51200.] (at C:\home\workspace\Paddle_release\paddle/fluid/operators/reshape_op.cc:224) - [operator < reshape2 > error] -``` - -解决方案：在使用 forward 函数实现 MNIST 网络的执行逻辑时，忽略了 self.pool_2_shape 变量的大小，重新设置 paddle.reshape 为 x = paddle.reshape(x, shape=[-1, self.pool_2_shape])即可 - - - -4、tensor 的值不能直接利用 - -报错：paddlepaddle 中的 value 不能直接拿出来用。 - -```python -TypeError: The type of 'shape' in reshape must be list[int] or tuple(int) in - Dygraph mode, but received , which contains Variable. -#错误代码：其中 stack_size, feat_size 为 tensor。 - -#改进加入 -# paddlepaddle code -stack_size = stack_size.numpy() -feat_size = feat_size.numpy() -``` - - - -5、Paddle 加载数据集报错，无法下载 MNIST 数据集，需要手动进行下载，（使用了多台电脑测试，均会出现此情况） - -```python -File "E:\anaconda\lib\site-packages\paddle\vision\datasets\mnist.py", line 98, in __init__ - self.image_path = _check_exists_and_download( - File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 207, in _check_exists_and_download - return paddle.dataset.common.download(url, module_name, md5) - File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 82, in download - raise RuntimeError("Cannot download {0} within retry limit {1}". -RuntimeError: Cannot download https://dataset.bj.bcebos.com/mnist/train-images-idx3-ubyte.gz within retry limit 3 -``` - -```python -File "E:\anaconda\lib\site-packages\paddle\vision\datasets\cifar.py", line 122, in __init__ - self.data_file = _check_exists_and_download( - File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 207, in _check_exists_and_download - return paddle.dataset.common.download(url, module_name, md5) - File "E:\anaconda\lib\site-packages\paddle\dataset\common.py", line 82, in download - raise RuntimeError("Cannot download {0} within retry limit {1}". -RuntimeError: Cannot download https://dataset.bj.bcebos.com/cifar/cifar-10-python.tar.gz within retry limit 3 -``` - -按照文档提供的'DatasetFolder', 'ImageFolder', 'MNIST', 'FashionMNIST', 'Flowers', 'Cifar10',多种数据集进行了下载测试，均无法在单机上加载数据集，需要手动下载数据集。 - - 且数据集的保存地址为一个缓存空间，用户在使用的时候可能找不到数据集，如/public/home/username/.cache/paddle/dataset 目录。 - - 而 pytorch 的加载数据集 API 会把数据集加载到当前目录，这一点的体验要优于 Paddle。 diff --git a/docs/faq/train_cn.md b/docs/faq/train_cn.md index 153b9eabe65..b8bc46bf421 100644 --- a/docs/faq/train_cn.md +++ b/docs/faq/train_cn.md @@ -110,7 +110,7 @@ export FLAGS_fast_eager_deletion_mode=1 export FLAGS_fraction_of_gpu_memory_to_use=0 ``` -详细请参考官方文档[存储分配与优化](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/performance_improving/singlenode_training_improving/memory_optimize.html) 调整相关配置。 +详细请参考官方文档[存储分配与优化](https://www.paddlepaddle.org.cn/documentation/docs/zh/dev_guides/api_contributing_guides/new_cpp_op_cn.html#xiancunyouhua) 调整相关配置。 此外，建议您使用[AI Studio 学习与 实训社区训练](https://aistudio.baidu.com/aistudio/index)，获取免费 GPU 算力，提升您的训练效率。 @@ -130,7 +130,7 @@ export FLAGS_fraction_of_gpu_memory_to_use=0 ##### 问题：如何处理变长 ID 导致程序内存占用过大的问题？ -+ 答复：请先参考[显存分配与优化文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/performance_improving/singlenode_training_improving/memory_optimize.html) 开启存储优化开关，包括显存垃圾及时回收和 Op 内部的输出复用输入等。若存储空间仍然不够，建议： ++ 答复：请先参考[显存分配与优化文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/dev_guides/api_contributing_guides/new_cpp_op_cn.html#xiancunyouhua) 开启存储优化开关，包括显存垃圾及时回收和 Op 内部的输出复用输入等。若存储空间仍然不够，建议： 1. 降低 `batch_size` ； 2. 对 index 进行排序，减少 padding 的数量。 diff --git a/docs/guides/10_contribution/community_contribution_cn.md b/docs/guides/10_contribution/community_contribution_cn.md deleted file mode 100644 index dea8119b37b..00000000000 --- a/docs/guides/10_contribution/community_contribution_cn.md +++ /dev/null @@ -1,83 +0,0 @@ -# 飞桨社区贡献指南 - -开源不仅开放源代码，更是一种分享的态度和沟通方式。每一个社区成员都可以通过贡献代码、贡献文档、加入特殊兴趣小组（SIG）、参与讨论与分享等方式，成为飞桨社区的重要一员，与社区开发者们共同进步。 - -## 社区支持 - -### QQ 群/微信群 - -飞桨官方组织了官方交流群，加入社群即可与其他开发者共同交流在深度学习中的所见所得。除此之外，飞桨还有各个垂直方向技术交流群，在这里你将能更快遇到志同道合的开发者，与他们一起交流学习。 -我们鼓励开发者们在社群中发挥所长，发表自己的技术见解，与社群开发者互帮互助，共同成长。 - -[点击加入 PaddlePaddle 开发者技术交流群](https://www.paddlepaddle.org.cn/support/news?action=detail&id=2439) - -## 飞桨论坛 - -作为深度学习爱好者的交流平台，包含原创博客、精品问答、产品交流等内容，为开发者提供纯粹、高质的技术交流平台。 - -[前往论坛](https://aistudio.baidu.com/paddle/forum) - -## 开源协作 - -### GitHub/Gitee - -在这里，开发者可以在开源项目中以 PR 的形式贡献代码，也可以在遇到问题时提交 issue 与相对应的开源贡献者进行交流沟通，飞桨的开源项目都会在这里向大家展示。 -当然，你也可以选择自己喜欢的开源项目，并为它投上一颗宝贵的 Star，让项目开发者感受到来自你的鼓励与支持。 - -GitHub: [PaddlePaddle/Paddle](https://github.com/PaddlePaddle/Paddle)、Gitee：[PaddlePaddle/Paddle](https://gitee.com/paddlepaddle/Paddle) - -### 飞桨特殊兴趣小组(PPSIG) - -由飞桨社区开发者负责和参与的 PPSIG，是飞桨开源社区的重要组成部分。目前有 12 个活跃的 PPSIG，围绕特定领域开展协作。还有更多 PPSIG 正在组建中，欢迎更多开发者们踊跃加入，参与飞桨生态共建。 - -[飞桨特殊兴趣小组(SIG)](https://www.paddlepaddle.org.cn/sig) - -## 技术论坛 - -论坛介绍：作为深度学习爱好者的交流平台，包含原创博客、精品问答、产品交流等内容，为开发者提供纯粹、高质的技术交流平台。 - -[前往论坛](https://aistudio.baidu.com/paddle/forum) - -## 技术布道 - -### 技术文章 - -欢迎向飞桨投稿有技术深度、逻辑流畅、内容清晰文章，我们期待更多来自开发者的优质内容在社区中进行分享，让更多的开发者看到、学习、关注到你和你的项目。入选了飞桨开发者说专栏的文章，将在飞桨公众号、知乎、开源中国 CSDN、飞桨论坛等全平台进行展示和传播。 - -**投稿要求** - -文章基本原则：Why？What？How？ - -- 为什么要做（Why） - - 项目背景主要包括：行业痛点、项目价值 - - 先说明当下遇到的行业痛点，从而引出阐述该项目的价值，包括但不局限于为生活提供便利、促进某个行业发展、趣味十足的小案例等 -- 项目是什么（What） - - 数据集：如用到了数据集，需说明数据集来源，测试集、训练集的比例或数量 - - 模型或算法：如使用了飞桨的模型或算法，请说明飞桨模型的名称和相关 GitHub 链接 - - 项目的效果：文章中最好能有图表来显示效果 -- 怎么做（How） - - 做事情要有始有终，文章也一样需要有一个好的 ending，结尾要包括项目的效果 - -[前往飞桨开发者说文章](https://mp.weixin.qq.com/mp/homepage?__biz=Mzg2OTEzODA5MA==&hid=16&sn=0561fc80d64fc079892454aafeb47bc4&scene=18) - -### 飞桨开发者说 Live 直播 - -通过飞桨开发者说直播的形式，分享在项目开发过程中的实践经验和踩坑破解的方法。我们相信，观看分享的观众也是你的同行者，会带给你无尽的力量。 - -[视频回放](https://space.bilibili.com/476867757/channel/detail?cid=176010) - -### 布道分享 - -以代码和项目为基础，在飞桨官方或开发者自组织的 Meetup/j 技术沙龙中向开发者宣讲技术，让飞桨 PaddlePaddle 提供的各种技术工具，变得更容易被理解和应用，而每一场分享布道的 Meetup，也会带给你更大的行业和技术影响力。 -请联系飞桨小助手（paddle-help）咨询投稿和参与方式。 - - -## 全国各地开源社区 - -飞桨领航团是飞桨开发者的兴趣社区，在各个城市/高校领航团团长及成员的热情支持下，飞桨领航团已建立 150 个社群，覆盖 29 个省级行政区，133 个高校，并且在持续增长中。开发者们可以参与丰富的本地技术沙龙、Meetup、及线上交流。 - -飞桨领航团面向所有对人工智能及深度学习领域感兴趣的开发者开放，欢迎广大开发者们加入领航团，结识更多本地技术同好，共享开源成果与快乐。 - - -[查看我所在的本地开源社区](https://www.paddlepaddle.org.cn/ppdenavigategroup) -加入飞桨领航团 QQ 群：484908840 diff --git a/docs/guides/10_contribution/faq_cn.rst b/docs/guides/10_contribution/faq_cn.rst deleted file mode 100644 index 6d1a7590991..00000000000 --- a/docs/guides/10_contribution/faq_cn.rst +++ /dev/null @@ -1,42 +0,0 @@ -.. _contribute_to_paddle_faq: - -################### -FAQ -################### - -.. contents:: - -1. CLA 签署不成功，怎么办？ ---------------------------- - -由于 `CLA `_ 是第三方开源库，有时候会不稳定。如果确定自己已签署 CLA，但 CLA 没触发成功，可尝试： - -* 关闭并重新开启本 PR，来重新触发 CLA。点击 :code:`Close pull request` ，再点击 :code:`Reopen pull request` ，并等待几分钟。 -* 如果上述操作重复 2 次仍未生效，请重新提一个 PR 或评论区留言。 - -2. CI 没有触发，怎么办？ ------------------------- - -* 请在 commit 信息中添加正确的 CI 触发规则： - - * develop 分支请添加 :code:`test=develop` - * release 分支请添加如 :code:`test=release/1.4` 来触发 release/1.4 分支 - * 文档预览请添加 :code:`test=document_preview` - -* 该 CI 触发规则以 commit 为单位，即对同一个 PR 来说，不管前面的 commit 是否已经添加，如果新 commit 想继续触发 CI，那么仍然需要添加。 -* 添加 CI 触发规则后，仍有部分 CI 没有触发：请关闭并重新开启本 PR，来重新触发 CI。 - - -3. CI 随机挂，即错误信息与本 PR 无关，怎么办？ --------------------------------------- - -由于 develop 分支代码的不稳定性，CI 可能会随机挂。 -如果确定 CI 错误和本 PR 无关，请在评论区贴上错误截图和错误链接。 - -4. 如何修改 API.spec？ ------------------------ - -为了保证 API 接口/文档的稳定性，我们对 API 进行了监控，即 API.spec 文件。 -修改方法请参考 `diff_api.py `_ 。 - -**注意**：提交 PR 后请查看下 diff，不要改到非本 PR 修改的 API 上。 diff --git a/docs/guides/10_contribution/hackathon_cn.md b/docs/guides/10_contribution/hackathon_cn.md deleted file mode 100644 index a5e0e3316dd..00000000000 --- a/docs/guides/10_contribution/hackathon_cn.md +++ /dev/null @@ -1,338 +0,0 @@ -## 一、活动介绍 - -第六期 PaddlePaddle Hackathon 飞桨黑客马拉松即将上线，本次活动是面向全球开发者的深度学习领域编程活动，鼓励开发者了解与参与飞桨深度学习开源项目的开发实践。 - -本次飞桨黑客马拉松由深度学习技术及应用国家工程研究中心主办，百度飞桨承办，活动包含以下四大赛道，等你来挑战！ - -- **开源贡献个人挑战赛** -- **飞桨护航计划集训营** -- **Fundable Projects** -- **优秀稿件征集与传播** - -对深度学习感兴趣的你，快来参加活动吧，更有机会获得现金奖励以及来自百度和开源社区技术大咖们的指导与经验分享，更多奖励等你来拿。 - -> 1. [第六期黑客松活动官网落地页](https://www.paddlepaddle.org.cn/PaddlePaddleHackathon-2024-3) -> 2. [第六期黑客松任务总览](https://github.com/PaddlePaddle/Paddle/issues/62904) - -### 1. 开源贡献个人挑战赛 - -贡献任务详见 issue： **https://github.com/PaddlePaddle/Paddle/issues/62905** - -以开源贡献为核心，发布 **5+** 开源项目的 **50+** 贡献任务，开源项目包括：飞桨核心框架、飞桨科学计算、生态伙伴项目等。**开发者使用 GitHub ID 报名，独立完成具有明确验收目标的贡献任务，获得对应的任务奖励。** - -#### 1.1 活动流程 - -**(1) 报名** - -在上述任务 issue 下**按格式**回复报名信息。 - -**报名格式参考：**`【报名】：2、3`，多个任务之间需要使用中文顿号分隔，报名多个连续任务可用横线表示，如 `2-5` - -**(2) 提交 RFC（可选）** - -请根据任务要求提交 RFC（Request for Comments）到 [PaddlePaddle/community/rfcs](https://github.com/PaddlePaddle/community/tree/master/rfcs) 目录，与飞桨团队沟通任务细节与后续开发规划。 - -> [!NOTE] -> 如果任务标注不需要 RFC，可跳过这一步直接进行代码开发。 - -**(3) 完成设计文档（可选）** - -任务要求提交 RFC 的，必须等 RFC 通过后再进行开发。RFC 合入后，任务榜单会更新任务状态为**完成设计文档**，后续开发需遵循 RFC 设计，否则提交会被拒绝（PR close）。 - -**(4) 提交 PR** - -开发完成后，提交 PR（Pull Requests）到任务指定的代码仓库，请确保你的 PR 包含清晰的代码结构和文档，以便审查（PR review）和合并（PR merge）。 - -> [!NOTE] -> **PR 提交格式要求：在 PR 的标题中以 【Hackathon 6th No.xxx】 开头，注明任务编号** - -**(5) 完成任务** - -在任务提交后 3 个工作日内，黑客松评审组将对 PR 进行验收并给出相应反馈：如果该提交符合要求，PR 顺利通过验收评审、 CI 测试并被合并（PR merge），视为任务完成，**该任务后续不再验收其他提交**。 - -**(6) 奖金发放** - -本期黑客松赛程全部结束后，活动组委会统一进行成果公示与奖金发放，你将根据任务难度获得相应的奖励，难度在任务发布时以 🌟 的数量标识，**一颗 🌟 对应￥ 2,000 奖金。** - -#### 1.2 活动日程 - -| **时间** | **日程** | -| :------------------------: | :------------------------: | -| **2024/03/20 19:00 GMT+8** | 活动正式上线，发布贡献任务 | -| **2024/05/29 19:00 GMT+8** | RFC 提交截止 | -| **2024/06/12 19:00 GMT+8** | PR 提交截止 | -| **2024/06/17 19:00 GMT+8** | 结果公示 | - -### 2. 飞桨护航计划集训营 - -> [!IMPORTANT] -> 护航计划集训营已于 **2024/01/22** 开放报名，**第二批补招**将于 **2024/03/20** 开放报名。 - -集训项目详见 issue： -- 第一批 https://github.com/PaddlePaddle/Paddle/issues/61006 -- 第二批 https://github.com/PaddlePaddle/Paddle/issues/62906 - -#### 2.1 活动流程 - -**(1) 报名** - -请在上述 [issue](https://github.com/PaddlePaddle/Paddle/issues/61006) 下**按格式**回复报名信息。 - -**报名格式参考：** - -``` -【报名】：1 -【其他意向】：2、4、6 -不接受调剂 -``` - -请注意报名序号填写第一意向，如有其他意向或接收调剂，可备注在报名邮件和 issue 评论区中。 - -报名完成后，请依据集训项目中的营员要求撰写简历，邮件发送至 [ext_paddle_oss@baidu.com](mailto:paddle-hack@baidu.com) 等候通知。 - -**(2) 导师面试** - -依据项目报名情况，导师进行简历筛选和面试，请及时关注预留的联系方式，**营员选拔信息每周更新到报名 issue 中。导师锁定营员后，营员需在 3 个工作日内答复是否参与该集训项目，需保证参与连续 3 个月的集训，并完成预期研发目标。营员确认后次日开启集训，由导师安排具体工作。** - -**(3) 集训期** - -营员以远程的方式深度参与开源项目开发实践，成果以 PR（Pull Requests）的形式贡献到指定代码仓库。导师指导营员按期完成研发目标，每 2 周更新一次项目报告，接收黑客松组委会监督。 - -**(4) 营员考核** - -黑客松组委会将于 **2024/06/03 - 2024/06/07 期间** 组织营员集中答辩，考核集训项目完成度与研发质量，**只有通过考核的营员才能获得对应的活动奖金与证书。** - -> [!IMPORTANT] -> 未满 3 个月集训时间的营员（如补招的营员），也将在 **2024/06/03 - 2024/06/07 期间** 提前参加答辩。但仍需继续进行集训开发，直至集训期满 3 个月。 - -**(5) 结营评估** - -依据营员开启集训的日期，在完成 3 个月集训后进行结营评估，评估结果分为 3 档，对应不同星级： - -- 及格：🌟🌟🌟 -- 良好：🌟🌟🌟🌟 -- 优秀：🌟🌟🌟🌟🌟 - -**(6) 奖金发放** - -本期护航计划集训营全部结束后，黑客松组委会统一进行成果公示与奖金发放，你将根据结营评估结果获得相应的奖励，**一颗 🌟 对应￥ 2,000 奖金。** - -#### 2.2 活动日程 - -| **时间** | **日程** | -| :-------------------------: | :------------------------: | -| **2024/01/22 18:00 GMT+8** | 发布集训项目，启动营员招募 | -| **2024/01/22 - 2023/02/23** | 公开接收简历，安排面试 | -| **2024/02/26 19:00 GMT+8** | 营员招募截止，公布招募名单 | -| **2024/02/29 16:00 GMT+8** | 开营仪式，集训营正式启动 | -| **2024/03/20 19:00 GMT+8** | 第二批补招开放 | -| **2023/06/03-2023/06/07** | 营员集中答辩 | -| **2023/06/12 23:59 GMT+8** | 集训营结营，公布考核结果 | - - -### 3. Fundable Projects - -贡献任务详见 issue： **https://github.com/PaddlePaddle/Paddle/issues/62908** - -定位**硬核项目**，要求开发者能够**独立拆解任务需求、梳理开发路径，并且号召社区共同实现某一功能需求**，最终以功能完成度为标准发放奖金。 - -#### 3.1 活动流程 - -**(1) 报名** - -在任务 issue 下**按格式**回复报名信息。 - -**报名格式参考：** - -``` -【报名】：1 -``` - -报名完成后，请提交 **简历** 或 **任务技术说明**，邮件发送至 ext_paddle_oss@baidu.com 等候通知。 - -> [!NOTE] -> 由于该赛道任务难度较大，同一任务允许有多名开发者报名，报名后将进行协商组队（基于报名者意愿的前提下）。 - -**(2) 筛选与沟通** - -依据报名情况、提交的简历或任务技术说明进行**筛选**，确保开发者能力与任务难度匹配。 - -报名通过后，会邀请入选成员进群，与飞桨研发进行技术沟通，最终敲定 RFC，并与队友确定分工（如有）。 - -**(3) 提交 RFC** - -根据沟通细节和项目技术梳理，主动进行任务拆解并提交 RFC（Request for Comments）到 [PaddlePaddle/community/rfcs](https://github.com/PaddlePaddle/community/tree/master/rfcs) 目录，参考 [RFC 设计文档模板](https://github.com/PaddlePaddle/community/blob/master/rfcs/design_template.md)，对任务进行技术方案设计，并与飞桨社区维护者共同讨论任务细节与分工。 - -**(4) 完成设计文档、发布 Tracking Issue** - -RFC 通过并合入后形成 Tracking Issue。原则上完成 RFC 的开发者会成为该任务的 leader，带领队员协作完成任务（任务 leader 优先获得参与决赛答辩的机会）。 - -> [!NOTE] -> -> 1. 优秀 RFC 案例：[将 xdoctest 引入到飞桨框架工作流中](https://github.com/PaddlePaddle/community/blob/master/rfcs/Docs/将%20xdoctest%20引入到飞桨框架工作流中.md) -> 2. 优秀 Tracking Issue 案例：[将 xdoctest 引入到飞桨框架工作流中 Tracking Issue](https://github.com/PaddlePaddle/Paddle/issues/54705) - -**(6) 任务开发、提交作品** - -根据任务描述进行开发，如有需要发动社区开发者协助的批量任务，可在群组联系相关负责人进行进一步沟通。 - -确保开发内容完整后，以 Pull Requests（PR）形式提交至指定的代码仓库，待 CI 大部分通过后，启动技术评审，技术评审启动后，你的 PR 会由任务导师 & 队员进行技术层面的评审与交流，你需要时刻关注反馈并与任务导师沟通作品细节，如果修改完毕，需要在 PR 下回复「修改完毕。 - -**(7) 作品验收与答辩** - -在通过技术评审后，你会获得技术 approve，PR 被 merge 表示你完成了一次开源贡献，可继续选择其他未完成的子任务进行开发。 - -在 **06/13-06/14** 期间，各团队需要准备材料参与答辩，飞桨研发将根据答辩情况和作品完成度进行最终评审。 - -**(8) 奖金发放** - -本期黑客松赛程全部结束后，活动组委会统一进行成果公示与奖金发放，你将根据任务难度获得相应的奖励，难度在任务发布时以 🌟 的数量标识，**一颗 🌟 对应￥ 2,000 奖金。** - -> [!IMPORTANT] -> 本赛道的各任务，项目完成度在 **80%** 以上可获得奖金，并按项目完成度比例发放。 - -#### 3.2 产出要求 - -1. 任务拆解 tracking issue -2. 答辩 PPT -3. 书面的技术报告 -4. 代码运行无误，通过社区 maintainers 的评审并合入代码仓库。 - - -#### 3.3 活动日程 - -| **时间** | **日程** | -| :------------------------: | :------------------------: | -| **2024/03/20 19:00 GMT+8** | 活动正式上线，发布贡献任务 | -| **2024/05/29 19:00 GMT+8** | RFC 提交截止 | -| **2024/06/12 19:00 GMT+8** | PR 提交截止 | -| **2023/06/13-2023/06/14** | 集中答辩 | -| **2024/06/17 19:00 GMT+8** | 结果公示 | - -### 4. 优秀稿件征集与传播 - -贡献任务详见 issue： **https://github.com/PaddlePaddle/Paddle/issues/62907** - -随着版本的更新，飞桨功能发生了非常大的变化。为了让更多的开发者了解这些信息，帮助飞桨传播正面影响力。本期黑客松新增**优秀稿件征集与传播**赛道，开发者可以撰写【选定知识点内】关于飞桨的任何文章，并通过任意形式进行影响力的传播。 - -#### 4.1 活动形式（两阶段） - -**阶段 1：撰写命题制的科技稿件** - -选手可以写 **【选定知识点内】** 的飞桨学习心得、如何使用飞桨等任意有助于扩大飞桨影响力的文章，内含飞桨知识点数量无限制。需要提 PR 到 [community/rfcs/Article)](https://github.com/PaddlePaddle/community/tree/master/rfcs/Article)，经研发审核合入后，可拿到阶段 1 的奖金。请注意： - -- **截止时间：** 截止 **5 月 15 日**，研发不再审核阶段 1 的稿件 PR； -- **奖金名额：** 不限制认领人数，一个题目可以有多篇稿件合入。共 20 个奖金名额，每个名额 0.5⭐️，先合入先得； - -> [!WARNING] -> 如果奖金名额用完但阶段 1 的截止时间没到，选手 A 可以继续进行阶段 1 的稿件创作，研发审核通过后可参加阶段 2 的影响力传播评选。即**选手 A 拿不到阶段 1 的奖金，但有可能拿到阶段 2 的奖品。** - -**阶段 2：影响力传播** - -当稿件成功审核通过后，选手需要以合入后的科技稿件为素材，进行影响力传播。可直接发文或制作成短视频等，形式不限。 - -- **传播渠道：** 选手可以选择自己熟悉的阵地，如微信公众号、知乎、B 站、小红书、YouTube、抖音等等；也可选择飞桨的[开源社区博客](https://pfcc.blog/)、[AIStudio 项目](https://aistudio.baidu.com/projectoverview/public)上进行传播，传播渠道范围和数量均不限。 -- **奖励评定：** 传播时间至少半个月，选手需提供后台数据（如小红书浏览量/赞/藏/评/粉丝）。结合后台数据和实际传播效果，最终评选 1/2/3 等奖，拿到阶段 2 的奖品（京东卡 1000/600/400）。 - -#### 4.2 活动流程 - -**(1) 报名** - -在上述任务 issue 下**按格式**回复报名信息。 - -**报名格式参考：**`【报名】：2、3`，多个任务之间需要使用中文顿号分隔，报名多个连续任务可用横线表示，如 `2-5` - -**(2) 提交 PR** - -稿件撰写完成后，以提交 PR（Pull Requests）到任务指定的代码仓库，请确保你的 PR 包含清晰、规范的文档结构和代码实例，以便审查（PR review）和合并（PR merge）。 - -> [!NOTE] -> **PR 提交格式要求：在 PR 的标题中以 【Hackathon 6th Article No.xxx】 开头，注明任务编号** - -**(3) 稿件审核通过** - -经研发审核无误后，你的稿件 PR 会被合入，并获得 1000 元（0.5🌟）奖励名额，**先合入先得**。当奖金名额占满后，仍能继续提交稿件，此时无法获得稿件奖励（阶段一），但可以参加后续的影响力传播评选。 - -**(4) 影响力传播** - -以合入的稿件为素材，选手进行影响力传播。传播时间至少半个月，传播形式和传播渠道均不限，最终选手需要提供后台数据（如小红书浏览量/赞/藏/评/粉丝）参与影响力传播的评选。组委会结合数据和实际传播效果，最终评选**一、二、三等奖**。 - -**(5) 奖金发放** - -本期黑客松赛程全部结束后，活动组委会统一进行成果公示与奖金发放，稿件奖励统一以半颗 🌟 的数量标识，**半颗 🌟 对应￥ 1,000 奖金。** - -影响力传播的奖励以京东卡的形式发放，一、二、三等奖分别对应 1000、600、400 元京东卡。 - -#### 4.3 活动日程 - -| **时间** | **日程** | -| :------------------------: | :--------------------: | -| **2024/03/20 19:00 GMT+8** | 活动正式上线，发布选题 | -| **2024/05/15 19:00 GMT+8** | 稿件提交截止 | -| **2024/06/12 19:00 GMT+8** | 影响力传播截止 | -| **2024/06/17 19:00 GMT+8** | 结果公示 | - -### **特别注意：** - -> [!WARNING] -> -> 1. **以上所有提及金额均为税前金额。** -> 2. **任务奖项评定需开发者提供完整作品（包括任务对应提交内容以及活动总结等）。** - -## 二、参与方式 - -### 1. 活动参与对象 - -本次活动面向全社会开放，不限年龄、身份、国籍，相关领域的个人、高校、科研机构、企业单位、初创团队等人员均可报名参赛。 - -#### 特别注意： - -（1）活动主办、合作项目单位，以及有机会接触任务背景的员工不得参与本次活动； - -（2）百度公司员工可参与比赛，但不得领取奖金。 - -### 2. 活动须知 - -（1）支持以个人或团队的形式报名并认领任务，允许跨单位自由组队； - -（2）报名须保证所提供的个人信息真实、准确、有效。如发放奖金或礼品时发现报名人员填写的报名信息与个人身份不相符，组委会将保留停止发放奖金或礼品的权利。 - -### 3. 活动报名 - -（1）活动期间均可报名，可在 **[活动页面](https://www.paddlepaddle.org.cn/PaddlePaddleHackathon-2024-3)** 查看具体活动信息； - -（2）请在赛道对应的 issue 下**按格式**回复报名信息，完成任务认领。 - -**补充信息**： - -（1）四大赛道开发任务不冲突，不限制报名次数，开发者可自由选择感兴趣的开发任务进行报名和参与； - -（2）**官方交流群：请扫码进第六期黑客松官方群~** - -加群二维码 - -### 4. 任务开发 - -当你完成相应开发后，相应的代码需要以 Pull Request 的形式上传到指定的代码仓库，黑客松小助手会自动更新你的进度到任务表单。黑客松评审组一般会在 3 个工作日内给出作品反馈，如果符合任务要求，则实时通报该任务已经被完成。 - -**注意**： - -（1）**任务进度由黑客松小助手每 2h 更新到 issue 榜单中；** - -（2）如果发现有恶意提交或抄袭等不正当行为，将会取消参与活动资格； - -（3）活动奖金与开发者获得的 🌟 数量挂钩，奖金发放上限￥ 30,000，**超出奖金上限将按照￥ 30,000 进行发放，但不限制开发者获取更多 🌟；** - -**（4）获取 🌟 数排名前 3 的开发者当选飞桨开源之星。** - -## 三、反作弊声明 - -- 参与者禁止注册多账户报名，一经发现取消获奖资格； -- 参与者禁止在指定考核技术能力的范围外利用规则漏洞或技术漏洞等不良途径参与活动，一经发现取消获奖资格； -- 可以接触到任务的相关人员，其提交作品不计入奖励范围。 - -## 四、其他 - -- 飞桨黑客松不收取任何报名费用； -- 请遵守飞桨开源社区 [Code-of-Conduct](https://github.com/PaddlePaddle/Paddle/blob/develop/CODE_OF_CONDUCT_cn.md)； -- 百度在法律法规许可范围内对本活动规则享有解释权。 diff --git a/docs/guides/10_contribution/img/change_base.png b/docs/guides/10_contribution/img/change_base.png deleted file mode 100644 index 7e08b96ec882c37132ff1ce3ec3044234eb84c7c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 26491 zcmagE1ymi)mNtw8O&~~chv4pR!GpU6cR9Gj!6kTbcXxMpcXxLU?))cj?%X?T{ol-- zwfb~*ch%ne*;ZA%J6KLe3;`Y+9t;c&K|)+u0SpXk71UONg$Di4ktqSez&@dv2?@zb 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