From fa7227e5fa956228ba3908fa0063187f8e447f3b Mon Sep 17 00:00:00 2001 From: Guanghua Yu <742925032@qq.com> Date: Fri, 31 Dec 2021 18:54:01 +0800 Subject: [PATCH] add ptq docs and demo (#5451) * add ptq docs and demo * fix readme * update readme --- .../Step6/deploy/ptq_python/README.md | 173 +++++++++++++ .../Step6/deploy/ptq_python/eval.py | 139 ++++++++++ .../Step6/deploy/ptq_python/post_quant.py | 113 +++++++++ .../tipc/images/post_training_quant_guide.png | Bin 0 -> 332703 bytes .../tipc/kl_infer_python/kl_infer_python.md | 7 - .../README.md | 0 .../tipc/ptq_infer_python/ptq_infer_python.md | 240 ++++++++++++++++++ .../test_ptq_infer_python.md} | 0 8 files changed, 665 insertions(+), 7 deletions(-) create mode 100644 tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py create mode 100644 tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/post_quant.py create mode 100644 tutorials/tipc/images/post_training_quant_guide.png delete mode 100644 tutorials/tipc/kl_infer_python/kl_infer_python.md rename tutorials/tipc/{kl_infer_python => ptq_infer_python}/README.md (100%) create mode 100644 tutorials/tipc/ptq_infer_python/ptq_infer_python.md rename tutorials/tipc/{kl_infer_python/test_kl_infer_python.md => ptq_infer_python/test_ptq_infer_python.md} (100%) diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/README.md b/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/README.md index e69de29b..4234cfac 100644 --- a/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/README.md +++ b/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/README.md @@ -0,0 +1,173 @@ +# MobileNetV3 + +## 目录 + + +- [1. 简介](#1) +- [2. 离线量化](#2) + - [2.1 准备Inference模型及环境](#2.1) + - [2.2 开始离线量化](#2.2) + - [2.3 验证推理结果](#2.3) +- [3. FAQ](#3) + + + + +## 1. 简介 + +Paddle中静态离线量化,使用少量校准数据计算量化因子,可以快速将FP32模型量化成低比特模型(比如最常用的int8量化)。使用该量化模型进行预测,可以减少计算量、降低计算内存、减小模型大小。 + +本文档主要基于Paddle的MobileNetV3模型进行离线量化。 + +更多关于Paddle 模型离线量化的介绍,可以参考[Paddle 离线量化官网教程](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/api_cn/static/quant/quantization_api.rst#quant_post_static)。 + + + + +## 2. 离线量化 + + + +### 2.1 准备Inference模型及环境 + +由于离线量化直接使用Inference模型进行量化,不依赖模型组网,所以需要提前准备好Inference模型. +我们准备好了动转静后的MobileNetv3 small的Inference模型,可以从[mobilenet_v3_small_infer](https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_infer.tar)直接下载。 + +```shell +wget https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_infer.tar +tar -xf mobilenet_v3_small_infer.tar +``` + +也可以按照[MobileNetv3 动转静流程](xxx),将MobileNetv3 small的模型转成Inference模型。 + + + +环境准备: + +- 安装PaddleSlim: +```shell +pip install paddleslim==2.2.1 +``` + +- 安装PaddlePaddle: +```shell +pip install paddlepaddle-gpu==2.2.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html +``` + +- 准备数据: + +请参考[数据准备文档](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6#32-%E5%87%86%E5%A4%87%E6%95%B0%E6%8D%AE)。 + +### 2.2 开始离线量化 + +启动离线量化: + +```bash +python post_quant.py --model_path=mobilenet_v3_small_infer/ \ + --model_filename=inference.pdmodel \ + --params_filename=inference.pdiparams \ + --data_dir=/path/dataset/ILSVRC2012/ \ + --use_gpu=True \ + --batch_size=32 \ + --batch_num=20 +``` + +部分离线量化日志如下: + +``` +Thu Dec 30 12:36:17-INFO: Collect quantized variable names ... +Thu Dec 30 12:36:17-INFO: Preparation stage ... +Thu Dec 30 12:36:27-INFO: Run batch: 0 +Thu Dec 30 12:37:10-INFO: Run batch: 5 +Thu Dec 30 12:37:43-INFO: Finish preparation stage, all batch:10 +Thu Dec 30 12:37:43-INFO: Sampling stage ... +Thu Dec 30 12:38:10-INFO: Run batch: 0 +Thu Dec 30 12:39:03-INFO: Run batch: 5 +Thu Dec 30 12:39:46-INFO: Finish sampling stage, all batch: 10 +Thu Dec 30 12:39:46-INFO: Calculate hist threshold ... +Thu Dec 30 12:39:47-INFO: Update the program ... +Thu Dec 30 12:39:49-INFO: The quantized model is saved in output/mv3_int8_infer +``` + +离线量化完成后,会在`output_dir`中生成量化后的Inference模型。 + + + +### 2.3 验证推理结果 + +- 量化推理模型重新命名: + +需要将`__model__`重命名为`inference.pdmodel`,将`__params__`重命名为`inference.pdiparams`。 + +正确的命名如下: +```shell +output/mv3_int8_infer/ + |----inference.pdiparams : 模型参数文件(原__params__文件) + |----inference.pdmodel : 模型结构文件(原__model__文件) +``` + +- 使用Paddle Inference测试模型推理结果是否正确: + +具体测试流程请参考[Inference推理文档](https://github.com/PaddlePaddle/models/blob/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/inference_python/README.md) + +如果您希望验证量化模型的在全量验证集上的精度,也可以按照下面的步骤进行操作: + +使用如下命令验证MobileNetv3 small模型的精度: + +- FP32模型: +```bash +python eval.py --model_path=mobilenet_v3_small_infer/ \ + --model_filename=inference.pdmodel \ + --params_filename=inference.pdiparams \ + --data_dir=/path/dataset/ILSVRC2012/ \ + --batch_size=128 \ + --use_gpu=True +``` + +FP32模型精度验证日志如下: + +``` +batch_id 300, acc1 0.602, acc5 0.825, avg time 0.00005 sec/img +batch_id 310, acc1 0.602, acc5 0.825, avg time 0.00005 sec/img +batch_id 320, acc1 0.602, acc5 0.825, avg time 0.00005 sec/img +batch_id 330, acc1 0.602, acc5 0.825, avg time 0.00005 sec/img +batch_id 340, acc1 0.601, acc5 0.825, avg time 0.00005 sec/img +batch_id 350, acc1 0.601, acc5 0.825, avg time 0.00005 sec/img +batch_id 360, acc1 0.602, acc5 0.826, avg time 0.00005 sec/img +batch_id 370, acc1 0.602, acc5 0.826, avg time 0.00005 sec/img +batch_id 380, acc1 0.602, acc5 0.825, avg time 0.00005 sec/img +batch_id 390, acc1 0.601, acc5 0.825, avg time 0.00005 sec/img +End test: test image 50000.0 +test_acc1 0.6015, test_acc5 0.8253, avg time 0.00005 sec/img +``` + +- 量化模型: +```shell +python eval.py --model_path=output/mv3_int8_infer/ \ + --model_filename=__model__ \ + --params_filename=__params__ \ + --data_dir=/path/dataset/ILSVRC2012/ \ + --batch_size=128 \ + --use_gpu=True +``` + +量化后模型精度验证日志如下: + +``` +batch_id 300, acc1 0.564, acc5 0.800, avg time 0.00006 sec/img +batch_id 310, acc1 0.562, acc5 0.798, avg time 0.00006 sec/img +batch_id 320, acc1 0.560, acc5 0.796, avg time 0.00006 sec/img +batch_id 330, acc1 0.556, acc5 0.792, avg time 0.00006 sec/img +batch_id 340, acc1 0.554, acc5 0.792, avg time 0.00006 sec/img +batch_id 350, acc1 0.552, acc5 0.790, avg time 0.00006 sec/img +batch_id 360, acc1 0.550, acc5 0.789, avg time 0.00006 sec/img +batch_id 370, acc1 0.551, acc5 0.789, avg time 0.00006 sec/img +batch_id 380, acc1 0.551, acc5 0.789, avg time 0.00006 sec/img +batch_id 390, acc1 0.553, acc5 0.790, avg time 0.00006 sec/img +End test: test image 50000.0 +test_acc1 0.5530, test_acc5 0.7905, avg time 0.00006 sec/img +``` + + + +## 3. FAQ diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py b/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py new file mode 100644 index 00000000..6d945488 --- /dev/null +++ b/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py @@ -0,0 +1,139 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import numpy as np +import time +import sys +import argparse +import math + +sys.path[0] = os.path.join( + os.path.dirname("__file__"), os.path.pardir, os.path.pardir) + +import paddle +import paddle.inference as paddle_infer + +from presets import ClassificationPresetEval +import paddlevision + + +def eval(): + # create predictor + model_file = os.path.join(FLAGS.model_path, FLAGS.model_filename) + params_file = os.path.join(FLAGS.model_path, FLAGS.params_filename) + config = paddle_infer.Config(model_file, params_file) + if FLAGS.use_gpu: + config.enable_use_gpu(1000, 0) + if not FLAGS.ir_optim: + config.switch_ir_optim(False) + + predictor = paddle_infer.create_predictor(config) + + input_names = predictor.get_input_names() + input_handle = predictor.get_input_handle(input_names[0]) + output_names = predictor.get_output_names() + output_handle = predictor.get_output_handle(output_names[0]) + + # prepare data + resize_size, crop_size = (256, 224) + val_dataset = paddlevision.datasets.ImageFolder( + os.path.join(FLAGS.data_dir, 'val'), + ClassificationPresetEval( + crop_size=crop_size, resize_size=resize_size)) + + eval_loader = paddle.io.DataLoader( + val_dataset, batch_size=FLAGS.batch_size, num_workers=5) + + cost_time = 0. + total_num = 0. + correct_1_num = 0 + correct_5_num = 0 + for batch_id, data in enumerate(eval_loader()): + # set input + img_np = np.array([tensor.numpy() for tensor in data[0]]) + label_np = np.array([tensor.numpy() for tensor in data[1]]) + + input_handle.reshape(img_np.shape) + input_handle.copy_from_cpu(img_np) + + # run + t1 = time.time() + predictor.run() + t2 = time.time() + cost_time += (t2 - t1) + + output_data = output_handle.copy_to_cpu() + + # calculate accuracy + for i in range(len(label_np)): + label = label_np[i][0] + result = output_data[i, :] + index = result.argsort() + total_num += 1 + if index[-1] == label: + correct_1_num += 1 + if label in index[-5:]: + correct_5_num += 1 + + if batch_id % 10 == 0: + acc1 = correct_1_num / total_num + acc5 = correct_5_num / total_num + avg_time = cost_time / total_num + print( + "batch_id {}, acc1 {:.3f}, acc5 {:.3f}, avg time {:.5f} sec/img". + format(batch_id, acc1, acc5, avg_time)) + + acc1 = correct_1_num / total_num + acc5 = correct_5_num / total_num + avg_time = cost_time / total_num + print("End test: test image {}".format(total_num)) + print("test_acc1 {:.4f}, test_acc5 {:.4f}, avg time {:.5f} sec/img".format( + acc1, acc5, avg_time)) + print("\n") + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + '--model_path', type=str, default="", help="The inference model path.") + parser.add_argument( + '--model_filename', + type=str, + default="model.pdmodel", + help="model filename") + parser.add_argument( + '--params_filename', + type=str, + default="model.pdiparams", + help="params filename") + parser.add_argument( + '--data_dir', + type=str, + default="dataset/ILSVRC2012/", + help="The ImageNet dataset root dir.") + parser.add_argument( + '--batch_size', type=int, default=10, help="Batch size.") + parser.add_argument( + '--use_gpu', type=bool, default=False, help=" Whether use gpu or not.") + parser.add_argument( + '--ir_optim', type=bool, default=False, help="Enable ir optim.") + + FLAGS = parser.parse_args() + + eval() diff --git a/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/post_quant.py b/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/post_quant.py new file mode 100644 index 00000000..5d78c8b7 --- /dev/null +++ b/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/post_quant.py @@ -0,0 +1,113 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import division +from __future__ import print_function + +import argparse +import os +import sys +import numpy as np +from PIL import Image + +sys.path[0] = os.path.join( + os.path.dirname("__file__"), os.path.pardir, os.path.pardir) + +import paddle +import paddlevision +from presets import ClassificationPresetEval + +from paddleslim.quant import quant_post_static + + +def sample_generator(loader): + def __reader__(): + for indx, data in enumerate(loader): + images = np.array(data[0]) + yield images + + return __reader__ + + +def main(): + paddle.enable_static() + place = paddle.CUDAPlace(0) if FLAGS.use_gpu else paddle.CPUPlace() + resize_size, crop_size = (256, 224) + val_dataset = paddlevision.datasets.ImageFolder( + os.path.join(FLAGS.data_dir, 'val'), + ClassificationPresetEval( + crop_size=crop_size, resize_size=resize_size)) + data_loader = paddle.io.DataLoader( + val_dataset, places=place, batch_size=FLAGS.batch_size) + quant_output_dir = os.path.join(FLAGS.output_dir, "mv3_int8_infer") + + exe = paddle.static.Executor(place) + quant_post_static( + executor=exe, + model_dir=FLAGS.model_path, + quantize_model_path=quant_output_dir, + sample_generator=sample_generator(data_loader), + model_filename=FLAGS.model_filename, + params_filename=FLAGS.params_filename, + batch_size=FLAGS.batch_size, + batch_nums=FLAGS.batch_num, + algo=FLAGS.algo, + hist_percent=FLAGS.hist_percent) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser("Quantization on ImageNet") + + parser.add_argument( + "--model_path", type=str, default=None, help="Inference model path") + parser.add_argument( + "--model_filename", + type=str, + default=None, + help="Inference model model_filename") + parser.add_argument( + "--params_filename", + type=str, + default=None, + help="Inference model params_filename") + parser.add_argument( + "--output_dir", type=str, default='output', help="save dir") + parser.add_argument( + '--data_dir', + default="/dataset/ILSVRC2012", + help='path to dataset (should have subdirectories named "train" and "val"' + ) + parser.add_argument( + '--use_gpu', + default=True, + type=bool, + help='Whether to use GPU or not.') + + # train + parser.add_argument( + "--batch_num", default=10, type=int, help="batch num for quant") + parser.add_argument( + "--batch_size", default=10, type=int, help="batch size for quant") + parser.add_argument( + '--algo', 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离线量化官网教程](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/api_cn/static/quant/quantization_api.rst#quant_post_static)。 + + + +## 2. 离线量化功能开发 + +Paddle 混合精度训练开发可以分为4个步骤,如下图所示。 + +
+ +
+ +其中设置了2个核验点,分别为: + +* 准备推理模型 +* 验证量化模型推理结果正确性 + + + +### 2.1 准备校准数据和环境 + +**【准备校准数据】** + +由于离线量化需要获得网络预测的每层的scale值,用来做数值范围的映射,所以需要适量的数据执行网络前向,故需要事先准备好校准数据集。 + +以ImageNet1k数据集为例,可参考[数据准备文档](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6#32-%E5%87%86%E5%A4%87%E6%95%B0%E6%8D%AE)。 + +**【准备开发环境】** + +- 确定已安装paddle,通过pip安装linux版本paddle命令如下,更多的版本安装方法可查看飞桨[官网](https://www.paddlepaddle.org.cn/) +- 确定已安装paddleslim,通过pip安装linux版本paddle命令如下,更多的版本安装方法可查看[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) + +``` +pip install paddlepaddle-gpu==2.2.1.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html +pip install paddleslim==2.2.1 +``` + + + +### 2.2 准备推理模型 + +**【基本流程】** + +准备推理模型分为三步: + +- Step1:定义继承自`paddle.nn.Layer`的网络模型 + +- Step2:使用`paddle.jit.save`接口对模型进行动转静,导出成Inference模型 + +- Step3:检查导出的路径下是否生成 `model.pdmodel` 和 `model.pdiparams` 文件 + +**【实战】** + +模型组网可以参考[mobilenet_v3](https://github.com/PaddlePaddle/models/blob/release/2.2/tutorials/mobilenetv3_prod/Step6/paddlevision/models/mobilenet_v3.py) + +```python +fp32_model = mobilenet_v3_small() +fp32_model.eval() +``` + +然后将模型进行动转静: + +```python +# save inference model +input_spec = paddle.static.InputSpec( + shape=[None, 3, 224, 224], dtype='float32') +fp32_output_model_path = os.path.join("mv3_fp32_infer", "model") +paddle.jit.save(fp32_model, fp32_output_model_path, [input_spec]) +``` + +会在`mv3_fp32_infer`文件夹下生成`model.pdmodel` 和 `model.pdiparams`两个文件。 + + + +### 2.3 准备离线量化代码 + +**【基本流程】** + +基于PaddleSlim,使用接口``paddleslim.quant.quant_post_static``对模型进行离线量化: + +- Step1:定义`sample_generator`,传入paddle.io.Dataloader实例化对象,用来遍历校准数据集 + +- Step2:定义Executor,由于离线量化模型是Inference模型,量化校准过程也需要在静态图下执行,所以需要定义静态图Executor,用来执行离线量化校准执行 + + +**【实战】** + +1)定义数据集,可以参考[Datasets定义](https://github.com/PaddlePaddle/models/blob/release/2.2/tutorials/mobilenetv3_prod/Step6/paddlevision/datasets/vision.py) + +2)定义`sample_generator`: + +```python +def sample_generator(loader): + def __reader__(): + for indx, data in enumerate(loader): + images = np.array(data[0]) + yield images + + return __reader__ +``` + +2)定义Executor: + +```python +use_gpu = True +place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace() +exe = paddle.static.Executor(place) +``` + + + + +### 2.4 开始离线量化 + +**【基本流程】** + +使用飞桨PaddleSlim中的`quant_post_static`接口开始进行离线量化: + +- Step1:导入`quant_post_static`接口 +```python +from paddleslim.quant import quant_post_static +``` + +- Step2:配置传入`quant_post_static`接口参数,开始离线量化 + +```python +fp32_model_dir = 'mv3_fp32_infer' +quant_output_dir = 'quant_model' +quant_post_static( + executor=exe, + model_dir=fp32_model_dir, + quantize_model_path=quant_output_dir, + sample_generator=sample_generator(data_loader), + model_filename='model.pdmodel', + params_filename='model.pdiparams', + batch_size=32, + batch_nums=10, + algo='KL') +``` + +- Step3:检查输出结果,确保离线量化后生成`__model__`和`__params__`文件。 + + +**【实战】** + +开始离线量化,具体可参考MobileNetv3[离线量化代码](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/post_quant.py)。 + + + + +### 2.5 验证推理结果正确性 + +**【基本流程】** + +使用Paddle Inference库测试离线量化模型,确保模型精度符合预期。 + +- Step1:初始化`paddle.inference`库并配置相应参数 + +```python +import paddle.inference as paddle_infer +model_file = os.path.join('quant_model', '__model__') +params_file = os.path.join('quant_model', '__params__') +config = paddle_infer.Config(model_file, params_file) +if FLAGS.use_gpu: + config.enable_use_gpu(1000, 0) +if not FLAGS.ir_optim: + config.switch_ir_optim(False) + +predictor = paddle_infer.create_predictor(config) +``` + +- Step2:配置预测库输入输出 + +```python +```python +input_names = predictor.get_input_names() +input_handle = predictor.get_input_handle(input_names[0]) +output_names = predictor.get_output_names() +output_handle = predictor.get_output_handle(output_names[0]) +``` + +- Step3:开始预测并检验结果正确性 + +```python +```python +input_handle.copy_from_cpu(img_np) +predictor.run() + output_data = output_handle.copy_to_cpu() +``` + +**【实战】** + + +1)初始化`paddle.inference`库并配置相应参数: + +具体可以参考MobileNetv3 [Inference模型测试代码](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py) + +2)配置预测库输入输出: + +具体可以参考MobileNetv3 [Inference模型测试代码](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py) + +3)开始预测: + +具体可以参考MobileNetv3 [Inference模型测试代码](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/eval.py) + +4)测试单张图像预测结果是否正确,可参考[Inference预测文档](https://github.com/PaddlePaddle/models/blob/release/2.2/docs/tipc/train_infer_python/infer_python.md) + +5)同时也可以测试量化模型和FP32模型的精度,确保量化后模型精度损失符合预期。参考[MobileNet量化模型精度验证文档](https://github.com/PaddlePaddle/models/tree/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/ptq_python/README.md) + + + +## 3. FAQ + +如果您在使用该文档完成离线量化的过程中遇到问题,可以给在[这里](https://github.com/PaddlePaddle/PaddleSlim/issues)提一个ISSUE,我们会高优跟进。 + +## 3.1 通用问题 + +- 如何选择离线量化方法? +选择合适的离线量化方法,比如`KL`、`hist`、`mse`等,具体离线量化方法选择可以参考API文档:[quant_post_static API文档](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/docs/zh_cn/api_cn/static/quant/quantization_api.rst#quant_post_static)。 diff --git a/tutorials/tipc/kl_infer_python/test_kl_infer_python.md b/tutorials/tipc/ptq_infer_python/test_ptq_infer_python.md similarity index 100% rename from tutorials/tipc/kl_infer_python/test_kl_infer_python.md rename to tutorials/tipc/ptq_infer_python/test_ptq_infer_python.md -- GitLab