!8165 【轻量级 PR】：update RELEASE.md.

Merge pull request !8165 from shenwei41/N/A

README.md

@@ -29,7 +29,7 @@ enrichment of the AI software/hardware application ecosystem.
 
 <img src="docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
 
-For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/en/0.3.0-alpha/architecture.html).
+For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/en/r0.5/architecture.html).
 
 ### Automatic Differentiation
 
@@ -66,7 +66,6 @@ MindSpore offers build options across multiple backends:
 | Ascend910 | Ubuntu-x86 | ✔️ |
 |  | EulerOS-x86 | ✔️ |
 |  | EulerOS-aarch64 | ✔️ |
-| GPU CUDA 9.2 | Ubuntu-x86 | ✔️ |
 | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
 | CPU | Ubuntu-x86 | ✔️ |
 |  | Windows-x86 | ✔️ |
@@ -76,7 +75,7 @@ For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an ex
 1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
 
     ```
-    pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.3.0-alpha/MindSpore/cpu/ubuntu_x86/mindspore-0.3.0-cp37-cp37m-linux_x86_64.whl
+    pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.5.0-beta/MindSpore/cpu/ubuntu_x86/mindspore-0.5.2-cp37-cp37m-linux_x86_64.whl
     ```
 
 2. Run the following command to verify the install.
@@ -133,8 +132,8 @@ currently the containerized build options are supported as follows:
 
     For `CPU` backend, you can directly pull and run the latest stable image using the below command:
     ```
-    docker pull mindspore/mindspore-cpu:0.3.0-alpha
-    docker run -it mindspore/mindspore-cpu:0.3.0-alpha /bin/bash
+    docker pull mindspore/mindspore-cpu:0.5.2-beta
+    docker run -it mindspore/mindspore-cpu:0.5.2-beta /bin/bash
     ```
 
 * GPU
@@ -151,8 +150,8 @@ currently the containerized build options are supported as follows:
 
     Then you can pull and run the latest stable image using the below command:
     ```
-    docker pull mindspore/mindspore-gpu:0.3.0-alpha
-    docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.3.0-alpha /bin/bash
+    docker pull mindspore/mindspore-gpu:0.5.2-beta
+    docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.5.2-beta /bin/bash
     ```
 
     To test if the docker image works, please execute the python code below and check the output:
@@ -187,7 +186,7 @@ please check out [docker](docker/README.md) repo for the details.
 
 ## Quickstart
 
-See the [Quick Start](https://www.mindspore.cn/tutorial/en/0.3.0-alpha/quick_start/quick_start.html)
+See the [Quick Start](https://www.mindspore.cn/tutorial/en/r0.5/quick_start/quick_start.html)
 to implement the image classification.
 
 ## Docs

RELEASE.md

@@ -1,3 +1,97 @@
+# Release 0.5.2-beta
+
+## Major Features and Improvements
+
+### Ascend 910 Training and Inference Framework
+* New models
+    * DenseNet121: a convolution based neural network for the task of image classification on ImageNet 2012 dataset.
+
+## Bugfixes
+* Models
+    * VGG16,Alexnet,GoogleNet,optimize network for better performance. ([!5539](https://gitee.com/mindspore/mindspore/pulls/5539))
+    * YOLOV3, fix yolov3_darknet53 dataset bug. ([!5658](https://gitee.com/mindspore/mindspore/pulls/5658))
+
+## Contributors
+Thanks goes to these wonderful people:
+
+Alexey Shevlyakov, avakh, baihuawei, BowenK, buxue, caifubi, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, Danish Farid, dayschan, dengwentao, dinghao, etone-chan, fangzehua, fary86, geekun, Giancarlo Colmenares, gong chen, gukecai, guohongzilong, hangangqiang, heleiwang, hesham, He Wei, hexia, hongxing, huangdongrun, huanghui, islam_amin, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, Jonathan Yan, jonyguo, Junhan Hu, Kang, kingfo, kouzhenzhong, kpy, kswang, laiyongqiang, leopz, liangzelang, lichenever, lihongkang, Li Hongzhang, lilei, limingqi107, lirongzhen1, liubuyu, liuchongming74, liuwenhao4, liuxiao, Lixia Chen, liyanliu, liyong, lizhenyu, lvliang, Mahdi, Margaret_wangrui, meixiaowei, ms_yan, nhussain, ougongchang, panfengfeng, panyifeng, peilinwang, Peilin Wang, pkuliuliu, qianlong, rick_sanchez, shibeiji, Shida He, shijianning, simson, sunsuodong, suteng, Tinazhang, Tron Zhang, unknown, VectorSL, wandongdong, wangcong, wangdongxu, wangdongxu6, wanghua, wangnan39, Wei Luning, wenchunjiang, wenkai, wilfChen, WilliamLian, wukesong, Xian Weizhao, Xiaoda Zhang, xiefangqi, xulei2020, xunxue, xutianchun, Yang, yanghaitao, yanghaitao1, yanghaoran, yangjie, yangjie159, YangLuo, Yanjun Peng, yankai, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yuchaojie, yujianfeng, zhangzhongpeng, zhangdengcheng, Zhang Qinghua, zhangyinxia, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang, wangdongxu
+
+Contributions of any kind are welcome!
+
+# Release 0.5.0-beta
+
+## Major Features and Improvements
+
+### Ascend 910 Training and Inference Framework
+* New models
+    * ResNext50: a simple, highly modularized network architecture using aggregated resdiual transformations for image classification on ImageNet 2012 dataset.
+    * MASS: a pre-training method for sequence to sequence based language generation tasks on Text Summarization and Conversational Response Generation using News Crawls 2007-2017 dataset, Gigaword corpus and Cornell movie dialog corpus.
+    * Transformer: a neural network architecture for language understanding on WMT 2014 English-German dataset.
+    * GCN：Graph Convolutional Networks for the task of classification of nodes in a graph on Cora and Citeseer datasets.
+    * GAT：an attention-based graph neural network for node classification on Cora and CiteSeer dataset.
+* Frontend and user interface
+    * Support tensor value and assignment of mixed tensor index in graph mode.
+    * Support tensor comparison, len operator, constexpr syntax, value and assignment of tensor index in pynative mode.
+    * Support converting MindSpore IR to pb format for infer model.
+    * Support print operator to write data directly on the hard disk.
+    * Add the double recursive programming solution for very high speed parallel strategy search in automatic parallel.
+    * User interfaces change log
+      * Allow the learning rate of AdamWeightDecayDynamicLR and Lamb to be 0([!1826](https://gitee.com/mindspore/mindspore/pulls/1826))
+      * Restricting the entire network input parameter is Tensor([!1967](https://gitee.com/mindspore/mindspore/pulls/1967))
+      * Turn shape and dtype into attributes instead of interfaces([!1919](https://gitee.com/mindspore/mindspore/pulls/1919))
+      * Delete multitypefungraph([!2116](https://gitee.com/mindspore/mindspore/pulls/2116))
+      * Refactor the callback module in an encapsulated way, use _CallbackManager instead of _build_callbacks([!2236](https://gitee.com/mindspore/mindspore/pulls/2236))
+      * Delete EmbeddingLookup([!2163](https://gitee.com/mindspore/mindspore/pulls/2163))
+      * Checkpoint add model_type([!2517](https://gitee.com/mindspore/mindspore/pulls/2517))
+* Executor and performance optimization
+    * Heterogeneous execution on CPU and Ascend devices supported, and is verified in Wide&Deep model.
+    * Quantitative training of MobileNetV2, Lenet and Resnet50 on Ascend-910 are supported.
+    * Support new fusion architecture, which can do fusion optimization across graphs and kernels to improve execution speed.
+* Data processing, augmentation, and save format
+    * Support data processing pipeline performance profiling.
+    * Support public dataset loading, such as CLUE and Coco.
+    * Support more text processing, such as more tokenizers and vocab data.
+    * Support MindRecord padded data.
+### Other Hardware Support
+* GPU platform
+    * New model supported: Bert / Wide&Deep.
+    * Support setting max device memory.
+* CPU platform
+    * New model supported: LSTM.
+
+## Bugfixes
+* Models
+    * Bert, Move Bert from `example` to `model_zoo`, optimize network for better performance. ([!1902](https://gitee.com/mindspore/mindspore/pulls/1902))
+    * VGG16, Move VGG16 from `example` to `model_zoo`, optimize network for better accuracy. ([!2645](https://gitee.com/mindspore/mindspore/pulls/2645))
+    * Alexnet, modify parameter setting to improve accuracy ([!1364](https://gitee.com/mindspore/mindspore/pulls/2370))
+    * Wide&Deep, Move Wide&Deep from `example` to `model_zoo`, optimize network for better performance. ([!2221](https://gitee.com/mindspore/mindspore/pulls/2221))
+* Python API
+    * Fix bug in auto cast([!1766](https://gitee.com/mindspore/mindspore/pulls/1766))
+    * Fix bug of register_backward_hook([!2148](https://gitee.com/mindspore/mindspore/pulls/2148))
+    * Fix bug of tuple args in pynative mode([!1878](https://gitee.com/mindspore/mindspore/pulls/1878))
+    * Fix bug of checking numbers of arguments and graph parameters([!1701](https://gitee.com/mindspore/mindspore/pulls/1701))
+* Executor
+    * Fix bug of loading input data repeatedly in pynative mode([!1966](https://gitee.com/mindspore/mindspore/pulls/1966))
+    * Fix bug of list cannot be used as input in pynative mode([!1765](https://gitee.com/mindspore/mindspore/pulls/1765))
+    * Fix bug of kernel select ([!2103](https://gitee.com/mindspore/mindspore/pulls/2103))
+    * Fix bug of pattern matching for batchnorm fusion in the case of auto mix precision.([!1851](https://gitee.com/mindspore/mindspore/pulls/1851))
+    * Fix bug of generate hccl's kernel info.([!2393](https://gitee.com/mindspore/mindspore/mindspore/pulls/2393))
+* GPU platform
+    * Fix bug of summary feature invalid([!2173](https://gitee.com/mindspore/mindspore/pulls/2173))
+* Data processing
+    * Fix bug of Cifar dataset reading([!2096](https://gitee.com/mindspore/mindspore/pulls/2096))
+    * Fix bug of C++ behavior in RandomCropAndResize([!2026](https://gitee.com/mindspore/mindspore/pulls/2026))
+    * Fix the bug of mindrecord shuffle([!2420](https://gitee.com/mindspore/mindspore/pulls/2420))
+* Third party
+    * Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).
+
+## Contributors
+Thanks goes to these wonderful people:
+
+Alexey Shevlyakov, avakh, baihuawei, BowenK, buxue, caifubi, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, Danish Farid, dayschan, dengwentao, dinghao, etone-chan, fangzehua, fary86, geekun, Giancarlo Colmenares, gong chen, gukecai, guohongzilong, hangangqiang, heleiwang, hesham, He Wei, hexia, hongxing, huangdongrun, huanghui, islam_amin, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, Jonathan Yan, jonyguo, Junhan Hu, Kang, kingfo, kouzhenzhong, kpy, kswang, laiyongqiang, leopz, liangzelang, lichenever, lihongkang, Li Hongzhang, lilei, limingqi107, lirongzhen1, liubuyu, liuchongming74, liuwenhao4, liuxiao, Lixia Chen, liyanliu, liyong, lizhenyu, lvliang, Mahdi, Margaret_wangrui, meixiaowei, ms_yan, nhussain, ougongchang, panfengfeng, panyifeng, peilinwang, Peilin Wang, pkuliuliu, qianlong, rick_sanchez, shibeiji, Shida He, shijianning, simson, sunsuodong, suteng, Tinazhang, Tron Zhang, unknown, VectorSL, wandongdong, wangcong, wangdongxu, wangdongxu6, wanghua, wangnan39, Wei Luning, wenchunjiang, wenkai, wilfChen, WilliamLian, wukesong, Xian Weizhao, Xiaoda Zhang, xiefangqi, xulei2020, xunxue, xutianchun, Yang, yanghaitao, yanghaitao1, yanghaoran, yangjie, yangjie159, YangLuo, Yanjun Peng, yankai, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yuchaojie, yujianfeng, zhangzhongpeng, zhangdengcheng, Zhang Qinghua, zhangyinxia, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang, wangdongxu
+
+Contributions of any kind are welcome!
+
 # Release 0.3.0-alpha
 
 ## Major Features and Improvements

Third_Party_Open_Source_Software_Notice

@@ -3638,6 +3638,61 @@ Copyright (C) 2001-2005, International Business Machines Corporation and others.
 Copyright (c) 1996-2016, International Business Machines Corporation
 Copyright (C) 1997-2010, International Business Machines
 
+Software: libtiff 4.1.0
+Copyright notice:
+Copyright © 2015 Open Microscopy Environment / University of Dundee
+Copyright (c) 2004, Andrey Kiselev <dron@ak4719.spb.edu>
+Copyright (c) 1990-1997 Sam Leffler
+Copyright (c) 1991-1997 Silicon Graphics, Inc.
+Copyright (c) 1988-1997 Sam Leffler
+Copyright (c) 1991-1997 Sam Leffler
+Use and Copyright
+Copyright (C) 1990, 1995 Frank D. Cringle.
+Copyright (c) 1994-1997 Sam Leffler
+Copyright (c) 1994-1997 Silicon Graphics, Inc.
+Copyright (c) 1997 Greg Ward Larson
+Copyright (c) 1997 Silicon Graphics, Inc.
+Copyright (c) 2010, Andrey Kiselev <dron@ak4719.spb.edu>
+Copyright (c) Joris Van Damme <info@awaresystems.be>
+Copyright (c) AWare Systems <http:www.awaresystems.be/>
+Copyright (c) 1996-1997 Sam Leffler
+Copyright (c) 1996 Pixar
+Copyright (c) 1995-1997 Sam Leffler
+Copyright (c) 1995-1997 Silicon Graphics, Inc.
+Copyright (c) 1988-1996 Sam Leffler
+Copyright (c) 1991-1996 Silicon Graphics, Inc.
+Copyright (c) 1992-1997 Sam Leffler
+Copyright (c) 1992-1997 Silicon Graphics, Inc.
+Copyright (c) 2018, Mapbox
+Copyright (c) 2017, Planet Labs
+Copyright (c) 1990 by Sun Microsystems, Inc.
+Copyright 1990 by Digital Equipment Corporation, Maynard, Massachusetts.
+Copyright 1991 by Digital Equipment Corporation, Maynard, Massachusetts.
+Copyright (c) 2002, Andrey Kiselev <dron@ak4719.spb.edu>
+Copyright (c) 2003 Ross Finlayson
+Additions (c) Richard Nolde 2006-2010
+Copyright (c) 2003, Andrey Kiselev <dron@ak4719.spb.edu>
+Copyright (c) 2000, Frank Warmerdam
+Copyright (c) 1987, 1993, 1994
+Copyright (c) 1989, 1993
+Copyright (c) 2009 Frank Warmerdam
+Copyright (c) 1987, 1993
+Copyright (c) 2005 The DragonFly Project.  All rights reserved.
+Copyright (c) 2003 Citrus Project,
+All rights reserved.
+Copyright (c) 1990, 1993
+Copyright (c) 1996 Mike Johnson
+Copyright (c) 1996 BancTec AB
+Copyright (c) 2004, Andrey Kiselev  <dron@ak4719.spb.edu>
+Copyright (c) 2012, Frank Warmerdam <warmerdam@pobox.com>
+Copyright (c) 2019, Even Rouault <even.rouault at spatialys.com>
+Copyright (c) 2007, Frank Warmerdam <warmerdam@pobox.com>
+Copyright (c) 2019, Thomas Bernard  <miniupnp@free.fr>
+Copyright (c) 2008, Andrey Kiselev  <dron@ak4719.spb.edu>
+Copyright (c) 1999, Frank Warmerdam
+Copyright (c) 1991-1996 Sam Leffler
+Copyright (c) 1996 USAF Phillips Laboratory
+
 Software: opencv 4.2.0
 Copyright notice:
 Copyright (C) 2016, NVIDIA Corporation, all rights reserved.
@@ -4095,3 +4150,11 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
+
+
+Software: bert eedf5716ce1268e56f0a50264a88cafad334ac61
+MindSpore only used file bert/tokenization.py
+Copyright notice:
+Copyright 2018 The Google AI Language Team Authors.
+
+Apache License, Version 2.0

akg

@@ -1 +1 @@
-Subproject commit c460176523d039c8995f1d71089753725ebc0792
+Subproject commit 7c462a5d5acd073dfeff4a49b28e01af55c31c55

build.sh

@@ -50,9 +50,9 @@ usage()
   echo "    -D Enable dumping of function graph ir, default on"
   echo "    -z Compile dataset & mindrecord, default on"
   echo "    -M Enable MPI and NCCL for GPU training, gpu default on"
-  echo "    -V Specify the minimum required cuda version, default CUDA 9.2"
+  echo "    -V Specify the minimum required cuda version, default CUDA 10.1"
   echo "    -I Compile predict, default off"
-  echo "    -K Compile with AKG, default off"
+  echo "    -K Compile with AKG, default on"
   echo "    -s Enable serving module, default off"
 }
 
@@ -88,7 +88,7 @@ checkopts()
   ENABLE_DUMP_IR="on"
   COMPILE_MINDDATA="on"
   ENABLE_MPI="off"
-  CUDA_VERSION="9.2"
+  CUDA_VERSION="10.1"
   COMPILE_PREDICT="off"
   USE_GLOG="on"
   PREDICT_PLATFORM=""
@@ -191,6 +191,10 @@ checkopts()
           usage
           exit 1
         fi
+        if [[ "X$OPTARG" == "X9.2" ]]; then
+          echo "Unsupported CUDA version 9.2"
+          exit 1
+        fi
         CUDA_VERSION="$OPTARG"
         ;;
       P)
@@ -248,7 +252,7 @@ checkopts()
   done
 }
 checkopts "$@"
-echo "---------------- mindspore: build start ----------------"
+echo "---------------- mindSpore: build start ----------------"
 mkdir -pv "${BUILD_PATH}/package/mindspore/lib"
 git submodule update --init graphengine
 if [[ "X$ENABLE_AKG" = "Xon" ]] && [[ "X$ENABLE_D" = "Xon" ]]; then
@@ -446,9 +450,9 @@ build_predict()
 
     cd "${BASEPATH}/predict/output/"
     if [[ "$PREDICT_PLATFORM" == "x86_64" ]]; then
-      tar -cf MSPredict-0.3.0-linux_x86_64.tar.gz include/ lib/ --warning=no-file-changed
+      tar -cf MSPredict-0.5.2-linux_x86_64.tar.gz include/ lib/ --warning=no-file-changed
     elif [[ "$PREDICT_PLATFORM" == "arm64" ]]; then
-      tar -cf MSPredict-0.3.0-linux_aarch64.tar.gz include/ lib/ --warning=no-file-changed
+      tar -cf MSPredict-0.5.2-linux_aarch64.tar.gz include/ lib/ --warning=no-file-changed
     fi
     echo "success to build predict project!"
 }

cmake/dependency_graphengine.cmake

@@ -45,7 +45,11 @@ else()
         set(ASCEND_PATH /usr/local/Ascend)
     endif()
     set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
-    set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
+    if (DEFINED ENV{ASCEND_CUSTOM_FWK_PATH})
+        set(ASCEND_RUNTIME_PATH $ENV{ASCEND_CUSTOM_FWK_PATH}/fwkacllib/lib64)
+    else ()
+        set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
+    endif ()
     find_library(c_sec libc_sec.so ${ASCEND_DRIVER_PATH})
     find_library(slog libslog.so ${ASCEND_DRIVER_PATH})
     find_library(mmpa libmmpa.so ${ASCEND_DRIVER_PATH})

cmake/external_libs/icu4c.cmake

@@ -8,8 +8,8 @@ else()
             VER 67.1
             LIBS ${LIB_ICU_COMMON} ${LIB_ICU_DATA} ${LIB_ICU_I18N}
             URL https://github.com/unicode-org/icu/archive/release-67-1.tar.gz
-            MD5 0c2662a2b0bc80b0eb56495205247c8f
-            CONFIGURE_COMMAND ./icu4c/source/runConfigureICU Linux --enable-rpath --disable-tests --disable-samples --disable-icuio --disable-extras ICU_DATA_FILTER_FILE=${CMAKE_SOURCE_DIR}/third_party/icu4c/filter.json
+            MD5 fd525fb47d8827b0b7da78b51dd2d93f
+            CONFIGURE_COMMAND ${CMAKE_SOURCE_DIR}/scripts/build_icu4c.sh
             )
     include_directories(${icu4c_INC})
     add_library(mindspore::icuuc ALIAS icu4c::${LIB_ICU_COMMON})

docker/mindspore-cpu/0.5.0-beta/Dockerfile

@@ -0,0 +1,67 @@
+FROM ubuntu:18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV PATH /usr/local/bin:$PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install MindSpore cpu whl package
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.5.0-beta/MindSpore/cpu/ubuntu_x86/mindspore-0.5.0-cp37-cp37m-linux_x86_64.whl

docker/mindspore-cpu/0.5.2-beta/Dockerfile

@@ -0,0 +1,67 @@
+FROM ubuntu:18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV PATH /usr/local/bin:$PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install MindSpore cpu whl package
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.5.2-beta/MindSpore/cpu/ubuntu_x86/mindspore-0.5.2-cp37-cp37m-linux_x86_64.whl

docker/mindspore-gpu/0.5.0-beta/Dockerfile

@@ -0,0 +1,83 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
+ENV PATH ${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV LD_LIBRARY_PATH ${OMPI_ROOT_PATH}/lib:$LD_LIBRARY_PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex \
+    libnccl2=2.4.8-1+cuda10.1 \
+    libnccl-dev=2.4.8-1+cuda10.1
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install openmpi (v3.1.5)
+RUN cd /tmp \
+    && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
+    && tar -xvf openmpi-3.1.5.tar.gz \
+    && cd /tmp/openmpi-3.1.5 \
+    && mkdir -p ${OMPI_ROOT_PATH} \
+    && ./configure --prefix=${OMPI_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -rf /tmp/openmpi-3.1.5 \
+    && rm -f /tmp/openmpi-3.1.5.tar.gz
+
+# Install MindSpore cuda-10.1 whl package
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.5.0-beta/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-0.5.0-cp37-cp37m-linux_x86_64.whl

docker/mindspore-gpu/0.5.2-beta/Dockerfile

@@ -0,0 +1,83 @@
+FROM nvidia/cuda:10.1-cudnn7-runtime-ubuntu18.04
+
+MAINTAINER leonwanghui <leon.wanghui@huawei.com>
+
+# Set env
+ENV PYTHON_ROOT_PATH /usr/local/python-3.7.5
+ENV OMPI_ROOT_PATH /usr/local/openmpi-3.1.5
+ENV PATH ${OMPI_ROOT_PATH}/bin:/usr/local/bin:$PATH
+ENV LD_LIBRARY_PATH ${OMPI_ROOT_PATH}/lib:$LD_LIBRARY_PATH
+
+# Install base tools
+RUN apt update \
+    && DEBIAN_FRONTEND=noninteractive apt install -y \
+    vim \
+    wget \
+    curl \
+    xz-utils \
+    net-tools \
+    openssh-client \
+    git \
+    ntpdate \
+    tzdata \
+    tcl \
+    sudo \
+    bash-completion
+
+# Install compile tools
+RUN DEBIAN_FRONTEND=noninteractive apt install -y \
+    gcc \
+    g++ \
+    zlibc \
+    make \
+    libgmp-dev \
+    patch \
+    autoconf \
+    libtool \
+    automake \
+    flex \
+    libnccl2=2.4.8-1+cuda10.1 \
+    libnccl-dev=2.4.8-1+cuda10.1
+
+# Set bash
+RUN echo "dash dash/sh boolean false" | debconf-set-selections
+RUN DEBIAN_FRONTEND=noninteractive dpkg-reconfigure dash
+
+# Install python (v3.7.5)
+RUN apt install -y libffi-dev libssl-dev zlib1g-dev libbz2-dev libncurses5-dev \
+    libgdbm-dev libgdbm-compat-dev liblzma-dev libreadline-dev libsqlite3-dev \
+    && cd /tmp \
+    && wget https://github.com/python/cpython/archive/v3.7.5.tar.gz \
+    && tar -xvf v3.7.5.tar.gz \
+    && cd /tmp/cpython-3.7.5 \
+    && mkdir -p ${PYTHON_ROOT_PATH} \
+    && ./configure --prefix=${PYTHON_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -f /usr/local/bin/python \
+    && rm -f /usr/local/bin/pip \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/python3.7 /usr/local/bin/python \
+    && ln -s ${PYTHON_ROOT_PATH}/bin/pip3.7 /usr/local/bin/pip \
+    && rm -rf /tmp/cpython-3.7.5 \
+    && rm -f /tmp/v3.7.5.tar.gz
+
+# Set pip source
+RUN mkdir -pv /root/.pip \
+    && echo "[global]" > /root/.pip/pip.conf \
+    && echo "trusted-host=mirrors.aliyun.com" >> /root/.pip/pip.conf \
+    && echo "index-url=http://mirrors.aliyun.com/pypi/simple/" >> /root/.pip/pip.conf
+
+# Install openmpi (v3.1.5)
+RUN cd /tmp \
+    && wget https://download.open-mpi.org/release/open-mpi/v3.1/openmpi-3.1.5.tar.gz \
+    && tar -xvf openmpi-3.1.5.tar.gz \
+    && cd /tmp/openmpi-3.1.5 \
+    && mkdir -p ${OMPI_ROOT_PATH} \
+    && ./configure --prefix=${OMPI_ROOT_PATH} \
+    && make -j4 \
+    && make install -j4 \
+    && rm -rf /tmp/openmpi-3.1.5 \
+    && rm -f /tmp/openmpi-3.1.5.tar.gz
+
+# Install MindSpore cuda-10.1 whl package
+RUN pip install --no-cache-dir https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.5.2-beta/MindSpore/gpu/ubuntu_x86/cuda-10.1/mindspore_gpu-0.5.2-cp37-cp37m-linux_x86_64.whl

example/nlp_to_mindrecord/CLUERNER2020/README.md

@@ -1,82 +0,0 @@
-# Guideline to Convert Training Data CLUERNER2020 to MindRecord For Bert Fine Tuning
-
-<!-- TOC -->
-
-- [What does the example do](#what-does-the-example-do)
-- [How to use the example to process CLUERNER2020](#how-to-use-the-example-to-process-cluerner2020)
-    - [Download CLUERNER2020 and unzip](#download-cluerner2020-and-unzip)
-    - [Generate MindRecord](#generate-mindrecord)
-    - [Create MindDataset By MindRecord](#create-minddataset-by-mindrecord)
-
-
-<!-- /TOC -->
-
-## What does the example do
-
-This example is based on [CLUERNER2020](https://www.cluebenchmarks.com/introduce.html) training data, generating MindRecord file, and finally used for Bert Fine Tuning progress.
-
-1.  run.sh: generate MindRecord entry script
-2.  run_read.py: create MindDataset by MindRecord entry script.
-    - create_dataset.py: use MindDataset to read MindRecord to generate dataset.
-
-## How to use the example to process CLUERNER2020
-
-Download CLUERNER2020, convert it to MindRecord, use MindDataset to read MindRecord.
-
-### Download CLUERNER2020 and unzip
-
-1. Download the training data zip.
-    > [CLUERNER2020 dataset download address](https://www.cluebenchmarks.com/introduce.html) **-> 任务介绍 -> CLUENER 细粒度命名实体识别 -> cluener下载链接**
-
-2. Unzip the training data to dir example/nlp_to_mindrecord/CLUERNER2020/cluener_public.
-    ```
-    unzip -d {your-mindspore}/example/nlp_to_mindrecord/CLUERNER2020/data/cluener_public cluener_public.zip
-    ```
-
-### Generate MindRecord
-
-1. Run the run.sh script.
-    ```bash
-    bash run.sh
-    ```
-
-2. Output like this:
-    ```
-    ...
-    [INFO] ME(17603:139620983514944,MainProcess):2020-04-28-16:56:12.498.235 [mindspore/mindrecord/filewriter.py:313] The list of mindrecord files created are: ['data/train.mindrecord'], and the list of index files are: ['data/train.mindrecord.db']
-    ...
-    [INFO] ME(17603,python):2020-04-28-16:56:13.400.175 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:667] WriteRawData] Write 1 records successfully.
-    [INFO] ME(17603,python):2020-04-28-16:56:13.400.863 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:667] WriteRawData] Write 1 records successfully.
-    [INFO] ME(17603,python):2020-04-28-16:56:13.401.534 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:667] WriteRawData] Write 1 records successfully.
-    [INFO] ME(17603,python):2020-04-28-16:56:13.402.179 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:667] WriteRawData] Write 1 records successfully.
-    [INFO] ME(17603,python):2020-04-28-16:56:13.402.702 [mindspore/ccsrc/mindrecord/io/shard_writer.cc:667] WriteRawData] Write 1 records successfully.
-    ...
-    [INFO] ME(17603:139620983514944,MainProcess):2020-04-28-16:56:13.431.208 [mindspore/mindrecord/filewriter.py:313] The list of mindrecord files created are: ['data/dev.mindrecord'], and the list of index files are: ['data/dev.mindrecord.db']
-    ```
-
-3. Generate files like this:
-    ```bash
-    $ ls output/
-    dev.mindrecord  dev.mindrecord.db  README.md  train.mindrecord  train.mindrecord.db
-    ```
-
-### Create MindDataset By MindRecord
-
-1. Run the run_read.sh script.
-    ```bash
-    bash run_read.sh
-    ```
-
-2. Output like this:
-    ```
-    ...
-    example 1340: input_ids: [ 101 3173 1290 4852 7676 3949  122 3299  123  126 3189 4510 8020 6381 5442 7357 2590 3636 8021 7676 3949 4294 1166 6121 3124 1277 6121 3124 7270 2135 3295 5789 3326 123  126 3189 1355 6134 1093 1325 3173 2399 6590 6791 8024  102    0    0    0    0    0    0    0    0    0    0   0    0    0    0    0    0    0    0]
-    example 1340: input_mask: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1  1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
-    example 1340: segment_ids: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
-    example 1340: label_ids: [ 0 18 19 20  2  4  0  0  0  0  0  0  0 34 36 26 27 28  0 34 35 35 35 35 35 35 35 35 35 36 26 27 28  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]
-    example 1341: input_ids: [ 101 1728  711 4293 3868 1168 2190 2150 3791  934 3633 3428 4638 6237 7025 8024 3297 1400 5310 3362 6206 5023 5401 1744 3297 7770 3791 7368  976 1139 1104 2137  511 102    0    0    0    0    0    0    0    0   0    0    0    0    0    0    0    0    0    0    0    0    0    0   0    0    0    0    0    0    0    0]
-    example 1341: input_mask: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
-    example 1341: segment_ids: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
-   example 1341: label_ids: [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 18 19 19 19 19 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]
-    ...
-    ```

example/nlp_to_mindrecord/CLUERNER2020/create_dataset.py

@@ -1,36 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""create MindDataset by MindRecord"""
-import mindspore.dataset as ds
-
-def create_dataset(data_file):
-    """create MindDataset"""
-    num_readers = 4
-    data_set = ds.MindDataset(dataset_file=data_file, num_parallel_workers=num_readers, shuffle=True)
-    index = 0
-    for item in data_set.create_dict_iterator():
-        # print("example {}: {}".format(index, item))
-        print("example {}: input_ids: {}".format(index, item['input_ids']))
-        print("example {}: input_mask: {}".format(index, item['input_mask']))
-        print("example {}: segment_ids: {}".format(index, item['segment_ids']))
-        print("example {}: label_ids: {}".format(index, item['label_ids']))
-        index += 1
-        if index % 1000 == 0:
-            print("read rows: {}".format(index))
-    print("total rows: {}".format(index))
-
-if __name__ == '__main__':
-    create_dataset('output/train.mindrecord')
-    create_dataset('output/dev.mindrecord')

example/nlp_to_mindrecord/CLUERNER2020/data/.gitignore

@@ -1 +0,0 @@
-cluener_public

example/nlp_to_mindrecord/CLUERNER2020/data/README.md

@@ -1 +0,0 @@
-## The input dataset

example/nlp_to_mindrecord/CLUERNER2020/output/README.md

@@ -1 +0,0 @@
-## output dir

example/nlp_to_mindrecord/CLUERNER2020/run.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-rm -f output/train.mindrecord*
-rm -f output/dev.mindrecord*
-
-if [ ! -d "../../../third_party/to_mindrecord/CLUERNER2020" ]; then
-    echo "The patch base dir ../../../third_party/to_mindrecord/CLUERNER2020 is not exist."
-    exit 1
-fi
-
-if [ ! -f "../../../third_party/patch/to_mindrecord/CLUERNER2020/data_processor_seq.patch" ]; then
-    echo "The patch file ../../../third_party/patch/to_mindrecord/CLUERNER2020/data_processor_seq.patch is not exist."
-    exit 1
-fi
-
-# patch for data_processor_seq.py
-patch -p0 -d ../../../third_party/to_mindrecord/CLUERNER2020/ -o data_processor_seq_patched.py < ../../../third_party/patch/to_mindrecord/CLUERNER2020/data_processor_seq.patch
-if [ $? -ne 0 ]; then
-    echo "Patch ../../../third_party/to_mindrecord/CLUERNER2020/data_processor_seq.py failed"
-    exit 1
-fi
-
-# use patched script
-python ../../../third_party/to_mindrecord/CLUERNER2020/data_processor_seq_patched.py \
---vocab_file=../../../third_party/to_mindrecord/CLUERNER2020/vocab.txt \
---label2id_file=../../../third_party/to_mindrecord/CLUERNER2020/label2id.json

example/nlp_to_mindrecord/enwiki/README.md

@@ -1,173 +0,0 @@
-# Guideline to Convert Training Data enwiki to MindRecord For Bert Pre Training
-
-<!-- TOC -->
-
-- [What does the example do](#what-does-the-example-do)
-- [How to use the example to process enwiki](#how-to-use-the-example-to-process-enwiki)
-    - [Download enwiki training data](#download-enwiki-training-data)
-    - [Process the enwiki](#process-the-enwiki)
-    - [Generate MindRecord](#generate-mindrecord)
-    - [Create MindDataset By MindRecord](#create-minddataset-by-mindrecord)
-
-
-<!-- /TOC -->
-
-## What does the example do
-
-This example is based on [enwiki](https://dumps.wikimedia.org/enwiki) training data, generating MindRecord file, and finally used for Bert network training.
-
-1.  run.sh: generate MindRecord entry script.
-2.  run_read.py: create MindDataset by MindRecord entry script.
-    - create_dataset.py: use MindDataset to read MindRecord to generate dataset.
-
-## How to use the example to process enwiki
-
-Download enwiki data, process it, convert it to MindRecord, use MindDataset to read MindRecord.
-
-### Download enwiki training data
-
-> [enwiki dataset download address](https://dumps.wikimedia.org/enwiki) **-> 20200501 -> enwiki-20200501-pages-articles-multistream.xml.bz2**
-
-### Process the enwiki
-
-1. Please follow the steps in [process enwiki](https://github.com/mlperf/training/tree/master/language_model/tensorflow/bert)
-- All permissions of this step belong to the link address website.
-
-### Generate MindRecord
-
-1. Run the run.sh script.
-    ```
-    bash run.sh input_dir output_dir vocab_file
-    ```
-    - input_dir: the directory which contains files like 'part-00251-of-00500'.
-    - output_dir: which will store the output mindrecord files.
-    - vocab_file: the vocab file which you can download from other opensource project.
-
-2. The output like this:
-    ```
-    ...
-    Begin preprocess Wed Jun 10 09:21:23 CST 2020
-    Begin preprocess input file: /mnt/data/results/part-00000-of-00500
-    Begin output file: part-00000-of-00500.mindrecord
-    Total task: 510, processing: 1
-    Begin preprocess input file: /mnt/data/results/part-00001-of-00500
-    Begin output file: part-00001-of-00500.mindrecord
-    Total task: 510, processing: 2
-    Begin preprocess input file: /mnt/data/results/part-00002-of-00500
-    Begin output file: part-00002-of-00500.mindrecord
-    Total task: 510, processing: 3
-    Begin preprocess input file: /mnt/data/results/part-00003-of-00500
-    Begin output file: part-00003-of-00500.mindrecord
-    Total task: 510, processing: 4
-    Begin preprocess input file: /mnt/data/results/part-00004-of-00500
-    Begin output file: part-00004-of-00500.mindrecord
-    Total task: 510, processing: 4
-    ...
-    ```
-
-3. Generate files like this:
-    ```bash
-    $ ls {your_output_dir}/
-    part-00000-of-00500.mindrecord part-00000-of-00500.mindrecord.db part-00001-of-00500.mindrecord part-00001-of-00500.mindrecord.db part-00002-of-00500.mindrecord part-00002-of-00500.mindrecord.db ...
-    ```
-
-### Create MindDataset By MindRecord
-
-1. Run the run_read.sh script.
-    ```bash
-    bash run_read.sh input_dir
-    ```
-    - input_dir: the directory which contains mindrecord files.
-
-2. The output like this:
-    ```
-    ...
-    example 633: input_ids: [  101  2043 19781  4305  2140  4520  2041  1010   103  2034  2455  2002
-      7879  2003  1996  2455  1997   103 26378  4160  1012   102  7291  2001
-      1996   103  1011  2343  1997  6327  1010  3423  1998   103  4262  2005
-      1996  2118  1997  2329  3996   103   102     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0]
-    example 633: input_mask: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
-     1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
-    example 633: segment_ids: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
-     1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
-     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
-    example 633: masked_lm_positions: [ 8 17 20 25 33 41  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
-      0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
-      0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
-      0  0  0  0]
-    example 633: masked_lm_ids: [ 1996 16137  1012  3580  2451  1012     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0     0     0     0     0     0     0     0     0
-         0     0     0     0]
-    example 633: masked_lm_weights: [1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
-     0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
-     0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
-     0. 0. 0. 0.]
-    example 633: next_sentence_labels: [1]
-    ...
-    ```

example/nlp_to_mindrecord/enwiki/create_dataset.py

@@ -1,43 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""create MindDataset by MindRecord"""
-import argparse
-import mindspore.dataset as ds
-
-def create_dataset(data_file):
-    """create MindDataset"""
-    num_readers = 4
-    data_set = ds.MindDataset(dataset_file=data_file, num_parallel_workers=num_readers, shuffle=True)
-    index = 0
-    for item in data_set.create_dict_iterator():
-        # print("example {}: {}".format(index, item))
-        print("example {}: input_ids: {}".format(index, item['input_ids']))
-        print("example {}: input_mask: {}".format(index, item['input_mask']))
-        print("example {}: segment_ids: {}".format(index, item['segment_ids']))
-        print("example {}: masked_lm_positions: {}".format(index, item['masked_lm_positions']))
-        print("example {}: masked_lm_ids: {}".format(index, item['masked_lm_ids']))
-        print("example {}: masked_lm_weights: {}".format(index, item['masked_lm_weights']))
-        print("example {}: next_sentence_labels: {}".format(index, item['next_sentence_labels']))
-        index += 1
-        if index % 1000 == 0:
-            print("read rows: {}".format(index))
-    print("total rows: {}".format(index))
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-i", "--input_file", nargs='+', type=str, help='Input mindreord file')
-    args = parser.parse_args()
-
-    create_dataset(args.input_file)

example/nlp_to_mindrecord/enwiki/run.sh

@@ -1,133 +0,0 @@
-#!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-if [ $# -ne 3 ]; then
-    echo "Usage: $0 input_dir output_dir vocab_file"
-    exit 1
-fi
-
-if [ ! -d $1 ]; then
-    echo "The input dir: $1 is not exist."
-    exit 1
-fi
-
-if [ ! -d $2 ]; then
-    echo "The output dir: $2 is not exist."
-    exit 1
-fi
-rm -fr $2/*.mindrecord*
-
-if [ ! -f $3 ]; then
-    echo "The vocab file: $3 is not exist."
-    exit 1
-fi
-
-data_dir=$1
-output_dir=$2
-vocab_file=$3
-file_list=()
-output_filename=()
-file_index=0
-
-function getdir() {
-    elements=`ls $1`
-    for element in ${elements[*]};
-    do
-        dir_or_file=$1"/"$element
-        if [ -d $dir_or_file ];
-        then
-            getdir $dir_or_file
-        else
-            file_list[$file_index]=$dir_or_file
-            echo "${dir_or_file}" | tr '/' '\n' > dir_file_list.txt   # dir dir file to mapfile
-            mapfile parent_dir < dir_file_list.txt
-            rm dir_file_list.txt >/dev/null 2>&1
-            tmp_output_filename=${parent_dir[${#parent_dir[@]}-1]}".mindrecord"
-            output_filename[$file_index]=`echo ${tmp_output_filename} | sed 's/ //g'`
-            file_index=`expr $file_index + 1`
-        fi
-    done
-}
-
-getdir "${data_dir}"
-# echo "The input files: "${file_list[@]}
-# echo "The output files: "${output_filename[@]}
-
-if [ ! -d "../../../third_party/to_mindrecord/zhwiki" ]; then
-    echo "The patch base dir ../../../third_party/to_mindrecord/zhwiki is not exist."
-    exit 1
-fi
-
-if [ ! -f "../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch" ]; then
-    echo "The patch file ../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch is not exist."
-    exit 1
-fi
-
-# patch for create_pretraining_data.py
-patch -p0 -d ../../../third_party/to_mindrecord/zhwiki/ -o create_pretraining_data_patched.py < ../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch
-if [ $? -ne 0 ]; then
-    echo "Patch ../../../third_party/to_mindrecord/zhwiki/create_pretraining_data.py failed"
-    exit 1
-fi
-
-# get the cpu core count
-num_cpu_core=`cat /proc/cpuinfo | grep "processor" | wc -l`
-avaiable_core_size=`expr $num_cpu_core / 3 \* 2`
-
-echo "Begin preprocess `date`"
-
-# using patched script to generate mindrecord
-file_list_len=`expr ${#file_list[*]} - 1`
-for index in $(seq 0 $file_list_len); do
-    echo "Begin preprocess input file: ${file_list[$index]}"
-    echo "Begin output file: ${output_filename[$index]}"
-    python ../../../third_party/to_mindrecord/zhwiki/create_pretraining_data_patched.py \
-        --input_file=${file_list[$index]} \
-        --output_file=${output_dir}/${output_filename[$index]} \
-        --partition_number=1 \
-        --vocab_file=${vocab_file} \
-        --do_lower_case=True \
-        --max_seq_length=512 \
-        --max_predictions_per_seq=76 \
-        --masked_lm_prob=0.15 \
-        --random_seed=12345 \
-        --dupe_factor=10 >/tmp/${output_filename[$index]}.log 2>&1 &
-    process_count=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-    echo "Total task: ${#file_list[*]}, processing: ${process_count}"
-    if [ $process_count -ge $avaiable_core_size ]; then
-        while [ 1 ]; do
-            process_num=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-            if [ $process_count -gt $process_num ]; then
-                process_count=$process_num
-                break;
-            fi
-            sleep 2
-        done
-    fi
-done
-
-process_num=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-while [ 1 ]; do
-    if [ $process_num -eq 0 ]; then
-        break;
-    fi
-    echo "There are still ${process_num} preprocess running ..."
-    sleep 2
-    process_num=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-done
-
-echo "Preprocess all the data success."
-echo "End preprocess `date`"

example/nlp_to_mindrecord/zhwiki/README.md

@@ -1,113 +0,0 @@
-# Guideline to Convert Training Data zhwiki to MindRecord For Bert Pre Training
-
-<!-- TOC -->
-
-- [What does the example do](#what-does-the-example-do)
-- [Run simple test](#run-simple-test)
-- [How to use the example to process zhwiki](#how-to-use-the-example-to-process-zhwiki)
-    - [Download zhwiki training data](#download-zhwiki-training-data)
-    - [Extract the zhwiki](#extract-the-zhwiki)
-    - [Generate MindRecord](#generate-mindrecord)
-    - [Create MindDataset By MindRecord](#create-minddataset-by-mindrecord)
-
-
-<!-- /TOC -->
-
-## What does the example do
-
-This example is based on [zhwiki](https://dumps.wikimedia.org/zhwiki) training data, generating MindRecord file, and finally used for Bert network training.
-
-1.  run.sh: generate MindRecord entry script.
-2.  run_read.py: create MindDataset by MindRecord entry script.
-    - create_dataset.py: use MindDataset to read MindRecord to generate dataset.
-
-## Run simple test
-
-Follow the step:
-
-```bash
-bash run_simple.sh         # generate output/simple.mindrecord* by ../../../third_party/to_mindrecord/zhwiki/sample_text.txt
-bash run_read_simple.sh    # use MindDataset to read output/simple.mindrecord*
-```
-
-## How to use the example to process zhwiki
-
-Download zhwiki data, extract it, convert it to MindRecord, use MindDataset to read MindRecord.
-
-### Download zhwiki training data
-
-> [zhwiki dataset download address](https://dumps.wikimedia.org/zhwiki) **-> 20200401 -> zhwiki-20200401-pages-articles-multistream.xml.bz2**
-
-- put the zhwiki-20200401-pages-articles-multistream.xml.bz2 in {your-mindspore}/example/nlp_to_mindrecord/zhwiki/data directory.
-
-### Extract the zhwiki
-
-1. Download [wikiextractor](https://github.com/attardi/wikiextractor) script to {your-mindspore}/example/nlp_to_mindrecord/zhwiki/data directory.
-
-    ```
-    $ ls data/
-    README.md  wikiextractor  zhwiki-20200401-pages-articles-multistream.xml.bz2
-    ```
-
-2. Extract the zhwiki.
-    ```python
-    python data/wikiextractor/WikiExtractor.py data/zhwiki-20200401-pages-articles-multistream.xml.bz2 --processes 4 --templates data/template --bytes 8M --min_text_length 0 --filter_disambig_pages --output data/extract
-    ```
-
-3. Generate like this:
-    ```
-    $ ls data/extract
-    AA AB
-    ```
-
-### Generate MindRecord
-
-1. Run the run.sh script.
-    ```
-    bash run.sh
-    ```
-    > Caution: This process maybe slow, please wait patiently. If you do not have a machine with enough memory and cpu, it is recommended that you modify the script to generate mindrecord in step by step.
-
-2. The output like this:
-    ```
-    patching file create_pretraining_data_patched.py (read from create_pretraining_data.py)
-    Begin preprocess input file: ./data/extract/AA/wiki_00
-    Begin output file: AAwiki_00.mindrecord
-    Total task: 5, processing: 1
-    Begin preprocess input file: ./data/extract/AA/wiki_01
-    Begin output file: AAwiki_01.mindrecord
-    Total task: 5, processing: 2
-    Begin preprocess input file: ./data/extract/AA/wiki_02
-    Begin output file: AAwiki_02.mindrecord
-    Total task: 5, processing: 3
-    Begin preprocess input file: ./data/extract/AB/wiki_02
-    Begin output file: ABwiki_02.mindrecord
-    Total task: 5, processing: 4
-    ...
-    ```
-
-3. Generate files like this:
-    ```bash
-    $ ls output/
-    AAwiki_00.mindrecord AAwiki_00.mindrecord.db AAwiki_01.mindrecord AAwiki_01.mindrecord.db AAwiki_02.mindrecord AAwiki_02.mindrecord.db ... ABwiki_00.mindrecord ABwiki_00.mindrecord.db ...
-    ```
-
-### Create MindDataset By MindRecord
-
-1. Run the run_read.sh script.
-    ```bash
-    bash run_read.sh
-    ```
-
-2. The output like this:
-    ```
-    ...
-    example 74: input_ids: [  101  8168   118 12847  8783  9977 15908   117  8256  9245 11643  8168  8847  8588 11575  8154  8228   143  8384  8376  9197 10241   103 10564 11421  8199 12268   112   161  8228 11541  9586  8436  8174  8363  9864  9702   103   103   119   103  9947 10564   103  8436  8806 11479   103  8912   119   103   103   103 12209  8303   103  8757  8824   117  8256   103  8619  8168 11541   102 11684  8196   103  8228  8847 11523   117  9059  9064 12410  8358  8181 10764   117 11167 11706  9920   148  8332 11390  8936  8205 10951 11997   103  8154   117   103  8670 10467   112   161 10951 13139 12413   117 10288   143 10425  8205   152 10795  8472  8196   103   161 12126  9172 13129 12106  8217  8174 12244  8205   143   103  8461  8277 10628   160  8221   119   102]
-    example 74: input_mask: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
-    example 74: segment_ids: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
-    example 74: masked_lm_positions: [  6  22  37  38  40  43  47  50  51  52  55  60  67  76  89  92  98 109 120   0]
-    example 74: masked_lm_ids: [ 8118  8165  8329  8890  8554  8458   119  8850  8565 10392  8174 11467  10291  8181  8549 12718 13139   112   158     0]
-    example 74: masked_lm_weights: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]
-    example 74: next_sentence_labels: [0]
-    ...
-    ```

example/nlp_to_mindrecord/zhwiki/create_dataset.py

@@ -1,43 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""create MindDataset by MindRecord"""
-import argparse
-import mindspore.dataset as ds
-
-def create_dataset(data_file):
-    """create MindDataset"""
-    num_readers = 4
-    data_set = ds.MindDataset(dataset_file=data_file, num_parallel_workers=num_readers, shuffle=True)
-    index = 0
-    for item in data_set.create_dict_iterator():
-        # print("example {}: {}".format(index, item))
-        print("example {}: input_ids: {}".format(index, item['input_ids']))
-        print("example {}: input_mask: {}".format(index, item['input_mask']))
-        print("example {}: segment_ids: {}".format(index, item['segment_ids']))
-        print("example {}: masked_lm_positions: {}".format(index, item['masked_lm_positions']))
-        print("example {}: masked_lm_ids: {}".format(index, item['masked_lm_ids']))
-        print("example {}: masked_lm_weights: {}".format(index, item['masked_lm_weights']))
-        print("example {}: next_sentence_labels: {}".format(index, item['next_sentence_labels']))
-        index += 1
-        if index % 1000 == 0:
-            print("read rows: {}".format(index))
-    print("total rows: {}".format(index))
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-i", "--input_file", nargs='+', type=str, help='Input mindreord file')
-    args = parser.parse_args()
-
-    create_dataset(args.input_file)

example/nlp_to_mindrecord/zhwiki/data/.gitignore

@@ -1,3 +0,0 @@
-wikiextractor/
-zhwiki-20200401-pages-articles-multistream.xml.bz2
-extract/

example/nlp_to_mindrecord/zhwiki/data/README.md

@@ -1 +0,0 @@
-## The input dataset

example/nlp_to_mindrecord/zhwiki/output/README.md

@@ -1 +0,0 @@
-## Output the mindrecord

example/nlp_to_mindrecord/zhwiki/run.sh

@@ -1,112 +0,0 @@
-#!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-rm -f output/*.mindrecord*
-
-data_dir="./data/extract"
-file_list=()
-output_filename=()
-file_index=0
-
-function getdir() {
-    elements=`ls $1`
-    for element in ${elements[*]};
-    do
-        dir_or_file=$1"/"$element
-        if [ -d $dir_or_file ];
-        then
-            getdir $dir_or_file
-        else
-            file_list[$file_index]=$dir_or_file
-            echo "${dir_or_file}" | tr '/' '\n' > dir_file_list.txt   # dir dir file to mapfile
-            mapfile parent_dir < dir_file_list.txt
-            rm dir_file_list.txt >/dev/null 2>&1
-            tmp_output_filename=${parent_dir[${#parent_dir[@]}-2]}${parent_dir[${#parent_dir[@]}-1]}".mindrecord"
-            output_filename[$file_index]=`echo ${tmp_output_filename} | sed 's/ //g'`
-            file_index=`expr $file_index + 1`
-        fi
-    done
-}
-
-getdir "${data_dir}"
-# echo "The input files: "${file_list[@]}
-# echo "The output files: "${output_filename[@]}
-
-if [ ! -d "../../../third_party/to_mindrecord/zhwiki" ]; then
-    echo "The patch base dir ../../../third_party/to_mindrecord/zhwiki is not exist."
-    exit 1
-fi
-
-if [ ! -f "../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch" ]; then
-    echo "The patch file ../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch is not exist."
-    exit 1
-fi
-
-# patch for create_pretraining_data.py
-patch -p0 -d ../../../third_party/to_mindrecord/zhwiki/ -o create_pretraining_data_patched.py < ../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch
-if [ $? -ne 0 ]; then
-    echo "Patch ../../../third_party/to_mindrecord/zhwiki/create_pretraining_data.py failed"
-    exit 1
-fi
-
-# get the cpu core count
-num_cpu_core=`cat /proc/cpuinfo | grep "processor" | wc -l`
-avaiable_core_size=`expr $num_cpu_core / 3 \* 2`
-
-echo "Begin preprocess `date`"
-
-# using patched script to generate mindrecord
-file_list_len=`expr ${#file_list[*]} - 1`
-for index in $(seq 0 $file_list_len); do
-    echo "Begin preprocess input file: ${file_list[$index]}"
-    echo "Begin output file: ${output_filename[$index]}"
-    python ../../../third_party/to_mindrecord/zhwiki/create_pretraining_data_patched.py \
-        --input_file=${file_list[$index]} \
-        --output_file=output/${output_filename[$index]} \
-        --partition_number=1 \
-        --vocab_file=../../../third_party/to_mindrecord/zhwiki/vocab.txt \
-        --do_lower_case=True \
-        --max_seq_length=128 \
-        --max_predictions_per_seq=20 \
-        --masked_lm_prob=0.15 \
-        --random_seed=12345 \
-        --dupe_factor=10 >/tmp/${output_filename[$index]}.log 2>&1 &   # user defined
-    process_count=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-    echo "Total task: ${#file_list[*]}, processing: ${process_count}"
-    if [ $process_count -ge $avaiable_core_size ]; then
-        while [ 1 ]; do
-            process_num=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-            if [ $process_count -gt $process_num ]; then
-                process_count=$process_num
-                break;
-            fi
-            sleep 2
-        done
-    fi
-done
-
-process_num=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-while [ 1 ]; do
-    if [ $process_num -eq 0 ]; then
-        break;
-    fi
-    echo "There are still ${process_num} preprocess running ..."
-    sleep 2
-    process_num=`ps -ef | grep create_pretraining_data_patched | grep -v grep | wc -l`
-done
-
-echo "Preprocess all the data success."
-echo "End preprocess `date`"

example/nlp_to_mindrecord/zhwiki/run_simple.sh

@@ -1,47 +0,0 @@
-#!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-rm -f output/simple.mindrecord*
-
-if [ ! -d "../../../third_party/to_mindrecord/zhwiki" ]; then
-    echo "The patch base dir ../../../third_party/to_mindrecord/zhwiki is not exist."
-    exit 1
-fi
-
-if [ ! -f "../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch" ]; then
-    echo "The patch file ../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch is not exist."
-    exit 1
-fi
-
-# patch for create_pretraining_data.py
-patch -p0 -d ../../../third_party/to_mindrecord/zhwiki/ -o create_pretraining_data_patched.py < ../../../third_party/patch/to_mindrecord/zhwiki/create_pretraining_data.patch
-if [ $? -ne 0 ]; then
-    echo "Patch ../../../third_party/to_mindrecord/zhwiki/create_pretraining_data.py failed"
-    exit 1
-fi
-
-# using patched script to generate mindrecord
-python ../../../third_party/to_mindrecord/zhwiki/create_pretraining_data_patched.py \
---input_file=../../../third_party/to_mindrecord/zhwiki/sample_text.txt \
---output_file=output/simple.mindrecord \
---partition_number=4 \
---vocab_file=../../../third_party/to_mindrecord/zhwiki/vocab.txt \
---do_lower_case=True \
---max_seq_length=128 \
---max_predictions_per_seq=20 \
---masked_lm_prob=0.15 \
---random_seed=12345 \
---dupe_factor=10    # user defined

example/resnet50_cifar10/README.md

@@ -1,137 +0,0 @@
-# ResNet-50 Example
-
-## Description
-
-This is an example of training ResNet-50 with CIFAR-10 dataset in MindSpore.
-
-## Requirements
-
-- Install [MindSpore](https://www.mindspore.cn/install/en).
-
-- Download the dataset CIFAR-10
-
-> Unzip the CIFAR-10 dataset to any path you want and the folder structure should include train and eval dataset as follows:
-> ```
-> .  
-> ├── cifar-10-batches-bin  # train dataset
-> └── cifar-10-verify-bin   # infer dataset
-> ```
-
-
-## Example structure
-
-```shell
-.
-├── config.py                       # parameter configuration
-├── dataset.py                      # data preprocessing
-├── eval.py                         # infer script
-├── lr_generator.py                 # generate learning rate for each step
-├── run_distribute_train.sh         # launch distributed training(8 pcs)
-├── run_infer.sh                    # launch infering
-├── run_standalone_train.sh         # launch standalone training(1 pcs)
-└── train.py                        # train script
-```
-
-
-## Parameter configuration
-
-Parameters for both training and inference can be set in config.py.
-
-```
-"class_num": 10,                  # dataset class num
-"batch_size": 32,                 # batch size of input tensor
-"loss_scale": 1024,               # loss scale
-"momentum": 0.9,                  # momentum
-"weight_decay": 1e-4,             # weight decay 
-"epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
-"buffer_size": 100,               # number of queue size in data preprocessing
-"image_height": 224,              # image height
-"image_width": 224,               # image width
-"save_checkpoint": True,          # whether save checkpoint or not
-"save_checkpoint_steps": 195,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
-"keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
-"save_checkpoint_path": "./",     # path to save checkpoint
-"warmup_epochs": 5,               # number of warmup epoch
-"lr_decay_mode": "poly"           # decay mode can be selected in steps, ploy and default
-"lr_init": 0.01,                  # initial learning rate
-"lr_end": 0.00001,                # final learning rate
-"lr_max": 0.1,                    # maximum learning rate
-```
-
-## Running the example
-
-### Train
-
-#### Usage
-
-```
-# distributed training
-Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
-
-# standalone training
-Usage: sh run_standalone_train.sh [DATASET_PATH]
-```
-
-
-#### Launch
-
-```
-# distribute training example
-sh run_distribute_train.sh rank_table.json ~/cifar-10-batches-bin
-
-# standalone training example
-sh run_standalone_train.sh ~/cifar-10-batches-bin
-```
-
-> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
-
-#### Result
-
-Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
-
-```
-# distribute training result(8 pcs)
-epoch: 1 step: 195, loss is 1.9601055
-epoch: 2 step: 195, loss is 1.8555021
-epoch: 3 step: 195, loss is 1.6707983
-epoch: 4 step: 195, loss is 1.8162166
-epoch: 5 step: 195, loss is 1.393667
-```
-
-### Infer
-
-#### Usage
-
-```
-# infer
-Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
-```
-
-#### Launch
-
-```
-# infer example
-sh run_infer.sh ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
-```
-
-> checkpoint can be produced in training process.
-
-#### Result
-
-Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
-
-```
-result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt
-```
-
-### Running on GPU
-```
-# distributed training example
-mpirun -n 8 python train.py --dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True
-
-# standalone training example
-python train.py --dataset_path=~/cifar-10-batches-bin --device_target="GPU"
-
-# infer example
-python eval.py --dataset_path=~/cifar10-10-verify-bin --device_target="GPU" --checkpoint_path=resnet-90_195.ckpt
-```

example/resnet50_cifar10/dataset.py

@@ -1,81 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""
-create train or eval dataset.
-"""
-import os
-import mindspore.common.dtype as mstype
-import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
-from mindspore.communication.management import init, get_rank, get_group_size
-from config import config
-
-
-def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
-    """
-    create a train or eval dataset
-
-    Args:
-        dataset_path(string): the path of dataset.
-        do_train(bool): whether dataset is used for train or eval.
-        repeat_num(int): the repeat times of dataset. Default: 1
-        batch_size(int): the batch size of dataset. Default: 32
-        target(str): the device target. Default: Ascend
-
-    Returns:
-        dataset
-    """
-    if target == "Ascend":
-        device_num = int(os.getenv("DEVICE_NUM"))
-        rank_id = int(os.getenv("RANK_ID"))
-    else:
-        init("nccl")
-        rank_id = get_rank()
-        device_num = get_group_size()
-
-    if device_num == 1:
-        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
-    else:
-        ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
-                               num_shards=device_num, shard_id=rank_id)
-
-    # define map operations
-    trans = []
-    if do_train:
-        trans += [
-            C.RandomCrop((32, 32), (4, 4, 4, 4)),
-            C.RandomHorizontalFlip(prob=0.5)
-        ]
-
-    trans += [
-        C.Resize((config.image_height, config.image_width)),
-        C.Rescale(1.0 / 255.0, 0.0),
-        C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
-        C.HWC2CHW()
-    ]
-
-    type_cast_op = C2.TypeCast(mstype.int32)
-
-    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
-    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
-
-    # apply batch operations
-    ds = ds.batch(batch_size, drop_remainder=True)
-
-    # apply dataset repeat operation
-    ds = ds.repeat(repeat_num)
-
-    return ds

example/resnet50_cifar10/eval.py

@@ -1,72 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""
-eval.
-"""
-import os
-import argparse
-from dataset import create_dataset
-from config import config
-from mindspore import context
-from mindspore.model_zoo.resnet import resnet50
-from mindspore.parallel._auto_parallel_context import auto_parallel_context
-from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
-from mindspore.train.model import Model, ParallelMode
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.communication.management import init, get_group_size
-
-parser = argparse.ArgumentParser(description='Image classification')
-parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
-parser.add_argument('--device_num', type=int, default=1, help='Device num.')
-parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
-parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
-parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
-parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
-parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
-args_opt = parser.parse_args()
-
-if __name__ == '__main__':
-    target = args_opt.device_target
-    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-    if not args_opt.do_eval and args_opt.run_distribute:
-        if target == "Ascend":
-            device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(device_id=device_id)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        elif target == "GPU":
-            init("nccl")
-            context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-
-    epoch_size = config.epoch_size
-    net = resnet50(class_num=config.class_num)
-    loss = SoftmaxCrossEntropyWithLogits(sparse=True)
-
-    if args_opt.do_eval:
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
-                                 target=target)
-        step_size = dataset.get_dataset_size()
-
-        if args_opt.checkpoint_path:
-            param_dict = load_checkpoint(args_opt.checkpoint_path)
-            load_param_into_net(net, param_dict)
-        net.set_train(False)
-
-        model = Model(net, loss_fn=loss, metrics={'acc'})
-        res = model.eval(dataset)
-        print("result:", res, "ckpt=", args_opt.checkpoint_path)

example/resnet50_cifar10/lr_generator.py

@@ -1,77 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""learning rate generator"""
-import numpy as np
-
-
-def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
-    """
-    generate learning rate array
-
-    Args:
-       global_step(int): total steps of the training
-       lr_init(float): init learning rate
-       lr_end(float): end learning rate
-       lr_max(float): max learning rate
-       warmup_epochs(int): number of warmup epochs
-       total_epochs(int): total epoch of training
-       steps_per_epoch(int): steps of one epoch
-       lr_decay_mode(string): learning rate decay mode, including steps, poly or default
-
-    Returns:
-       np.array, learning rate array
-    """
-    lr_each_step = []
-    total_steps = steps_per_epoch * total_epochs
-    warmup_steps = steps_per_epoch * warmup_epochs
-    if lr_decay_mode == 'steps':
-        decay_epoch_index = [0.3 * total_steps, 0.6 * total_steps, 0.8 * total_steps]
-        for i in range(total_steps):
-            if i < decay_epoch_index[0]:
-                lr = lr_max
-            elif i < decay_epoch_index[1]:
-                lr = lr_max * 0.1
-            elif i < decay_epoch_index[2]:
-                lr = lr_max * 0.01
-            else:
-                lr = lr_max * 0.001
-            lr_each_step.append(lr)
-    elif lr_decay_mode == 'poly':
-        if warmup_steps != 0:
-            inc_each_step = (float(lr_max) - float(lr_init)) / float(warmup_steps)
-        else:
-            inc_each_step = 0
-        for i in range(total_steps):
-            if i < warmup_steps:
-                lr = float(lr_init) + inc_each_step * float(i)
-            else:
-                base = (1.0 - (float(i) - float(warmup_steps)) / (float(total_steps) - float(warmup_steps)))
-                lr = float(lr_max) * base * base
-                if lr < 0.0:
-                    lr = 0.0
-            lr_each_step.append(lr)
-    else:
-        for i in range(total_steps):
-            if i < warmup_steps:
-                lr = lr_init + (lr_max - lr_init) * i / warmup_steps
-            else:
-                lr = lr_max - (lr_max - lr_end) * (i - warmup_steps) / (total_steps - warmup_steps)
-            lr_each_step.append(lr)
-
-    current_step = global_step
-    lr_each_step = np.array(lr_each_step).astype(np.float32)
-    learning_rate = lr_each_step[current_step:]
-
-    return learning_rate

example/resnet50_cifar10/run_infer.sh

@@ -1,64 +0,0 @@
-#!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-
-if [ $# != 2 ]
-then 
-    echo "Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]"
-exit 1
-fi
-
-get_real_path(){
-  if [ "${1:0:1}" == "/" ]; then
-    echo "$1"
-  else
-    echo "$(realpath -m $PWD/$1)"
-  fi
-}
-
-PATH1=$(get_real_path $1)
-PATH2=$(get_real_path $2)
-
-
-if [ ! -d $PATH1 ]
-then 
-    echo "error: DATASET_PATH=$1 is not a directory"
-exit 1
-fi 
-
-if [ ! -f $PATH2 ]
-then 
-    echo "error: CHECKPOINT_PATH=$2 is not a file"
-exit 1
-fi 
-
-ulimit -u unlimited
-export DEVICE_NUM=1
-export DEVICE_ID=0
-export RANK_SIZE=$DEVICE_NUM
-export RANK_ID=0
-
-if [ -d "infer" ];
-then
-    rm -rf ./infer
-fi
-mkdir ./infer
-cp *.py ./infer
-cp *.sh ./infer
-cd ./infer || exit
-env > env.log
-echo "start infering for device $DEVICE_ID"
-python eval.py --do_eval=True --dataset_path=$PATH1 --checkpoint_path=$PATH2 &> log &
-cd ..

example/resnet50_cifar10/train.py

@@ -1,97 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""train_imagenet."""
-import os
-import argparse
-import numpy as np
-from dataset import create_dataset
-from lr_generator import get_lr
-from config import config
-from mindspore import context
-from mindspore import Tensor
-from mindspore.model_zoo.resnet import resnet50
-from mindspore.parallel._auto_parallel_context import auto_parallel_context
-from mindspore.nn.optim.momentum import Momentum
-from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
-
-from mindspore.train.model import Model, ParallelMode
-
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-from mindspore.train.loss_scale_manager import FixedLossScaleManager
-from mindspore.communication.management import init, get_rank, get_group_size
-
-parser = argparse.ArgumentParser(description='Image classification')
-parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
-parser.add_argument('--device_num', type=int, default=1, help='Device num.')
-parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
-parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
-parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
-parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
-args_opt = parser.parse_args()
-
-
-if __name__ == '__main__':
-    target = args_opt.device_target
-    ckpt_save_dir = config.save_checkpoint_path
-    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-    np.random.seed(1)
-    if not args_opt.do_eval and args_opt.run_distribute:
-        if target == "Ascend":
-            device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
-                                enable_auto_mixed_precision=True)
-            init()
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
-            ckpt_save_dir = config.save_checkpoint_path
-        elif target == "GPU":
-            context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False)
-            init("nccl")
-            context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
-    epoch_size = config.epoch_size
-    net = resnet50(class_num=config.class_num)
-
-    if args_opt.do_train:
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
-                                 repeat_num=epoch_size, batch_size=config.batch_size, target=target)
-        step_size = dataset.get_dataset_size()
-
-        loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-        lr = Tensor(get_lr(global_step=0, lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
-                           warmup_epochs=config.warmup_epochs, total_epochs=epoch_size, steps_per_epoch=step_size,
-                           lr_decay_mode='poly'))
-        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
-                       config.weight_decay, config.loss_scale)
-        if target == 'GPU':
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, is_grad=False, reduction='mean')
-            opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum)
-            model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
-        else:
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-            model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
-                          amp_level="O2", keep_batchnorm_fp32=False)
-
-        time_cb = TimeMonitor(data_size=step_size)
-        loss_cb = LossMonitor()
-        cb = [time_cb, loss_cb]
-        if config.save_checkpoint:
-            config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
-                                         keep_checkpoint_max=config.keep_checkpoint_max)
-            ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
-            cb += [ckpt_cb]
-        model.train(epoch_size, dataset, callbacks=cb)

example/resnet50_imagenet2012/README.md

@@ -1,150 +0,0 @@
-# ResNet-50 Example
-
-## Description
-
-This is an example of training ResNet-50 with ImageNet2012 dataset in MindSpore.
-
-## Requirements
-
-- Install [MindSpore](https://www.mindspore.cn/install/en).
-
-- Download the dataset ImageNet2012 
-
-> Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:
-> ```
-> .  
-> ├── ilsvrc                  # train dataset
-> └── ilsvrc_eval             # infer dataset
-> ```
-
-
-## Example structure
-
-```shell
-.
-├── crossentropy.py                 # CrossEntropy loss function
-├── config.py                       # parameter configuration
-├── dataset.py                      # data preprocessing
-├── eval.py                         # infer script
-├── lr_generator.py                 # generate learning rate for each step
-├── run_distribute_train.sh         # launch distributed training(8 pcs)
-├── run_infer.sh                    # launch infering
-├── run_standalone_train.sh         # launch standalone training(1 pcs)
-└── train.py                        # train script
-```
-
-
-## Parameter configuration
-
-Parameters for both training and inference can be set in config.py.
-
-```
-"class_num": 1001,                # dataset class number
-"batch_size": 32,                 # batch size of input tensor
-"loss_scale": 1024,               # loss scale
-"momentum": 0.9,                  # momentum optimizer
-"weight_decay": 1e-4,             # weight decay 
-"epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
-"pretrained_epoch_size": 1,       # epoch size that model has been trained before load pretrained checkpoint
-"buffer_size": 1000,              # number of queue size in data preprocessing
-"image_height": 224,              # image height
-"image_width": 224,               # image width
-"save_checkpoint": True,          # whether save checkpoint or not
-"save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
-"keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
-"save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
-"warmup_epochs": 0,               # number of warmup epoch
-"lr_decay_mode": "cosine",        # decay mode for generating learning rate
-"label_smooth": True,             # label smooth
-"label_smooth_factor": 0.1,       # label smooth factor
-"lr_init": 0,                     # initial learning rate
-"lr_max": 0.1,                    # maximum learning rate
-```
-
-## Running the example
-
-### Train
-
-#### Usage
-
-```
-# distributed training
-Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
-
-# standalone training
-Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
-
-```
-
-
-#### Launch
-
-```bash
-# distributed training example(8 pcs)
-sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
-
-# If you want to load pretrained ckpt file
-sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc ./pretrained.ckpt
-
-# standalone training example(1 pcs)
-sh run_standalone_train.sh dataset/ilsvrc
-
-# If you want to load pretrained ckpt file
-sh run_standalone_train.sh dataset/ilsvrc ./pretrained.ckpt
-```
-
-> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
-
-#### Result
-
-Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.
-
-```
-# distribute training result(8 pcs)
-epoch: 1 step: 5004, loss is 4.8995576
-epoch: 2 step: 5004, loss is 3.9235563
-epoch: 3 step: 5004, loss is 3.833077
-epoch: 4 step: 5004, loss is 3.2795618
-epoch: 5 step: 5004, loss is 3.1978393
-```
-
-### Infer
-
-#### Usage
-
-```
-# infer
-Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]
-```
-
-#### Launch
-
-```bash
-# infer with checkpoint
-sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-90_5004.ckpt
-```
-
-> checkpoint can be produced in training process.
-
-#### Result
-
-Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.
-
-```
-result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt
-```
-
-### Running on GPU
-```
-# distributed training example
-mpirun -n 8 python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --run_distribute=True
-
-# standalone training example
-python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU"
-
-# standalone training example with pretrained checkpoint
-python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --pre_trained=pretrained.ckpt
-
-# infer example
-python eval.py --dataset_path=dataset/ilsvrc/val --device_target="GPU" --checkpoint_path=resnet-90_5004ss.ckpt
-```

example/resnet50_imagenet2012/dataset.py

@@ -1,85 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""
-create train or eval dataset.
-"""
-import os
-import mindspore.common.dtype as mstype
-import mindspore.dataset.engine as de
-import mindspore.dataset.transforms.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as C2
-from mindspore.communication.management import init, get_rank, get_group_size
-
-def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
-    """
-    create a train or eval dataset
-
-    Args:
-        dataset_path(string): the path of dataset.
-        do_train(bool): whether dataset is used for train or eval.
-        repeat_num(int): the repeat times of dataset. Default: 1
-        batch_size(int): the batch size of dataset. Default: 32
-        target(str): the device target. Default: Ascend
-
-    Returns:
-        dataset
-    """
-    if target == "Ascend":
-        device_num = int(os.getenv("DEVICE_NUM"))
-        rank_id = int(os.getenv("RANK_ID"))
-    else:
-        init("nccl")
-        rank_id = get_rank()
-        device_num = get_group_size()
-
-    if device_num == 1:
-        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
-    else:
-        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
-                                     num_shards=device_num, shard_id=rank_id)
-
-    image_size = 224
-    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
-    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
-
-    # define map operations
-    if do_train:
-        trans = [
-            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
-            C.RandomHorizontalFlip(prob=0.5),
-            C.Normalize(mean=mean, std=std),
-            C.HWC2CHW()
-        ]
-    else:
-        trans = [
-            C.Decode(),
-            C.Resize((256, 256)),
-            C.CenterCrop(image_size),
-            C.Normalize(mean=mean, std=std),
-            C.HWC2CHW()
-        ]
-
-    type_cast_op = C2.TypeCast(mstype.int32)
-
-    ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
-    ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
-
-    # apply batch operations
-    ds = ds.batch(batch_size, drop_remainder=True)
-
-    # apply dataset repeat operation
-    ds = ds.repeat(repeat_num)
-
-    return ds

example/resnet50_imagenet2012/eval.py

@@ -1,62 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""
-eval.
-"""
-import os
-import argparse
-from dataset import create_dataset
-from config import config
-from mindspore import context
-from mindspore.model_zoo.resnet import resnet50
-from mindspore.train.model import Model
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from crossentropy import CrossEntropy
-
-parser = argparse.ArgumentParser(description='Image classification')
-parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
-parser.add_argument('--device_num', type=int, default=1, help='Device num.')
-parser.add_argument('--do_train', type=bool, default=False, help='Do train or not.')
-parser.add_argument('--do_eval', type=bool, default=True, help='Do eval or not.')
-parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
-parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
-parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
-args_opt = parser.parse_args()
-target = args_opt.device_target
-context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-if target == "Ascend":
-    device_id = int(os.getenv('DEVICE_ID'))
-    context.set_context(device_id=device_id)
-
-if __name__ == '__main__':
-
-    net = resnet50(class_num=config.class_num)
-    if not config.use_label_smooth:
-        config.label_smooth_factor = 0.0
-    loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-
-    if args_opt.do_eval:
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size,
-                                 target=target)
-        step_size = dataset.get_dataset_size()
-
-        if args_opt.checkpoint_path:
-            param_dict = load_checkpoint(args_opt.checkpoint_path)
-            load_param_into_net(net, param_dict)
-        net.set_train(False)
-
-        model = Model(net, loss_fn=loss, metrics={'acc'})
-        res = model.eval(dataset)
-        print("result:", res, "ckpt=", args_opt.checkpoint_path)

example/resnet50_imagenet2012/train.py

@@ -1,122 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# 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.
-# ============================================================================
-"""train_imagenet."""
-import os
-import argparse
-import numpy as np
-from dataset import create_dataset
-from lr_generator import get_lr
-from config import config
-from mindspore import context
-from mindspore import Tensor
-from mindspore.model_zoo.resnet import resnet50
-from mindspore.parallel._auto_parallel_context import auto_parallel_context
-from mindspore.nn.optim.momentum import Momentum
-
-from mindspore.train.model import Model, ParallelMode
-
-from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
-from mindspore.train.loss_scale_manager import FixedLossScaleManager
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from mindspore.communication.management import init, get_rank, get_group_size
-import mindspore.nn as nn
-import mindspore.common.initializer as weight_init
-from crossentropy import CrossEntropy
-
-parser = argparse.ArgumentParser(description='Image classification')
-parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
-parser.add_argument('--device_num', type=int, default=1, help='Device num.')
-parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
-parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
-parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
-parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
-parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
-args_opt = parser.parse_args()
-
-if __name__ == '__main__':
-    target = args_opt.device_target
-    ckpt_save_dir = config.save_checkpoint_path
-    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
-    np.random.seed(1)
-    if not args_opt.do_eval and args_opt.run_distribute:
-        if target == "Ascend":
-            device_id = int(os.getenv('DEVICE_ID'))
-            context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id,
-                                enable_auto_mixed_precision=True)
-            init()
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([107, 160])
-            ckpt_save_dir = config.save_checkpoint_path
-        elif target == "GPU":
-            context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False)
-            init("nccl")
-            context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
-
-    epoch_size = config.epoch_size
-    net = resnet50(class_num=config.class_num)
-
-    # weight init
-    if args_opt.pre_trained:
-        param_dict = load_checkpoint(args_opt.pre_trained)
-        load_param_into_net(net, param_dict)
-        epoch_size = config.epoch_size - config.pretrained_epoch_size
-    else:
-        for _, cell in net.cells_and_names():
-            if isinstance(cell, nn.Conv2d):
-                cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(),
-                                                                    cell.weight.default_input.shape,
-                                                                    cell.weight.default_input.dtype).to_tensor()
-            if isinstance(cell, nn.Dense):
-                cell.weight.default_input = weight_init.initializer(weight_init.TruncatedNormal(),
-                                                                    cell.weight.default_input.shape,
-                                                                    cell.weight.default_input.dtype).to_tensor()
-    if not config.use_label_smooth:
-        config.label_smooth_factor = 0.0
-
-    loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-
-    if args_opt.do_train:
-        dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
-                                 repeat_num=epoch_size, batch_size=config.batch_size, target=target)
-        step_size = dataset.get_dataset_size()
-
-        loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-        lr = get_lr(lr_init=config.lr_init, lr_end=0.0, lr_max=config.lr_max, warmup_epochs=config.warmup_epochs,
-                    total_epochs=config.epoch_size, steps_per_epoch=step_size, lr_decay_mode='cosine')
-        if args_opt.pre_trained:
-            lr = lr[config.pretrained_epoch_size * step_size:]
-        lr = Tensor(lr)
-
-        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
-                       config.weight_decay, config.loss_scale)
-        if target == "Ascend":
-            model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
-                          amp_level="O2", keep_batchnorm_fp32=False)
-        elif target == "GPU":
-            model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
-
-
-        time_cb = TimeMonitor(data_size=step_size)
-        loss_cb = LossMonitor()
-        cb = [time_cb, loss_cb]
-        if config.save_checkpoint:
-            config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*step_size,
-                                         keep_checkpoint_max=config.keep_checkpoint_max)
-            ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
-            cb += [ckpt_cb]
-        model.train(epoch_size, dataset, callbacks=cb)

graphengine

@@ -1 +1 @@
-Subproject commit dda72a48c7e0033389bd377c5804d485fdf3112d
+Subproject commit 1c2672868fda8b1d012c99e5aca73725ac869ba9

mindspore/_checkparam.py

@@ -593,6 +593,17 @@ def check_bool(input_param):
     raise TypeError("Input type must be bool!")
 
 
+def check_string(input_param, valid_values):
+    """String type judgment."""
+    if isinstance(input_param, str) and input_param in valid_values:
+        return input_param
+    if len(valid_values) == 1:
+        raise ValueError(f'Input should be str and must be {valid_values[0]},'
+                         f' but got {input_param}.')
+    raise ValueError(f'Input should be str and must be one of {valid_values},'
+                     f' but got {input_param}.')
+
+
 def check_input_format(input_param):
     """Judge input format."""
     if input_param == "NCHW":

mindspore/ccsrc/CMakeLists.txt

@@ -164,7 +164,11 @@ if (ENABLE_D)
         endif ()
         set(ASCEND_DRIVER_PATH ${ASCEND_PATH}/driver/lib64/common)
         set(ASCEND_DRIVER_BACK_PATH ${ASCEND_PATH}/driver/lib64/driver)
-        set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
+        if (DEFINED ENV{ASCEND_CUSTOM_FWK_PATH})
+            set(ASCEND_RUNTIME_PATH $ENV{ASCEND_CUSTOM_FWK_PATH}/fwkacllib/lib64)
+        else ()
+            set(ASCEND_RUNTIME_PATH ${ASCEND_PATH}/fwkacllib/lib64)
+        endif ()
     endif()
 
     MESSAGE("USE DAV LIB PATH: ${ASCEND_PATH}")
@@ -247,7 +251,7 @@ add_library(inference SHARED
         ${CMAKE_CURRENT_SOURCE_DIR}/session/session.cc
         ${LOAD_ONNX_SRC}
         )
-target_link_libraries(inference PRIVATE ${PYTHON_LIBRARY} ${SECUREC_LIBRARY}
+target_link_libraries(inference PRIVATE ${PYTHON_LIBRARIES} ${SECUREC_LIBRARY}
         -Wl,--whole-archive mindspore -Wl,--no-whole-archive mindspore_gvar mindspore::protobuf)
 
 if (ENABLE_CPU)

mindspore/ccsrc/dataset/core/config_manager.cc

@@ -41,6 +41,7 @@ Status ConfigManager::FromJson(const nlohmann::json &j) {
   set_worker_connector_size(j.value("workerConnectorSize", worker_connector_size_));
   set_op_connector_size(j.value("opConnectorSize", op_connector_size_));
   set_seed(j.value("seed", seed_));
+  set_monitor_sampling_interval(j.value("monitorSamplingInterval", monitor_sampling_interval_));
   return Status::OK();
 }
 

mindspore/ccsrc/dataset/engine/datasetops/source/sampler/sampler.cc

@@ -91,11 +91,14 @@ void Sampler::Print(std::ostream &out, bool show_all) const {
 Status Sampler::GetAllIdsThenReset(py::array *data) {
   std::unique_ptr<DataBuffer> db;
   std::shared_ptr<Tensor> sample_ids;
+  TensorRow sample_row;
 
   // A call to derived class to get sample ids wrapped inside a buffer
   RETURN_IF_NOT_OK(GetNextSample(&db));
   // Get the only tensor inside the buffer that contains the actual SampleIds for the entire epoch
-  RETURN_IF_NOT_OK(db->GetTensor(&sample_ids, 0, 0));
+  RETURN_IF_NOT_OK(db->GetRow(0, &sample_row));
+  sample_ids = sample_row[0];
+
   // check this buffer is not a ctrl buffer
   CHECK_FAIL_RETURN_UNEXPECTED(db->buffer_flags() == DataBuffer::kDeBFlagNone, "ERROR ctrl buffer received");
   {

mindspore/ccsrc/dataset/engine/gnn/graph.cc

@@ -149,14 +149,37 @@ Status Graph::GetAllNeighbors(const std::vector<NodeIdType> &node_list, NodeType
   return Status::OK();
 }
 
+Status Graph::CheckSamplesNum(NodeIdType samples_num) {
+  NodeIdType all_nodes_number =
+    std::accumulate(node_type_map_.begin(), node_type_map_.end(), 0,
+                    [](NodeIdType t1, const auto &t2) -> NodeIdType { return t1 + t2.second.size(); });
+  if ((samples_num < 1) || (samples_num > all_nodes_number)) {
+    std::string err_msg = "Wrong samples number, should be between 1 and " + std::to_string(all_nodes_number) +
+                          ", got " + std::to_string(samples_num);
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+
 Status Graph::GetSampledNeighbors(const std::vector<NodeIdType> &node_list,
                                   const std::vector<NodeIdType> &neighbor_nums,
                                   const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) {
   CHECK_FAIL_RETURN_UNEXPECTED(!node_list.empty(), "Input node_list is empty.");
   CHECK_FAIL_RETURN_UNEXPECTED(neighbor_nums.size() == neighbor_types.size(),
                                "The sizes of neighbor_nums and neighbor_types are inconsistent.");
+  for (const auto &num : neighbor_nums) {
+    RETURN_IF_NOT_OK(CheckSamplesNum(num));
+  }
+  for (const auto &type : neighbor_types) {
+    if (node_type_map_.find(type) == node_type_map_.end()) {
+      std::string err_msg = "Invalid neighbor type:" + std::to_string(type);
+      RETURN_STATUS_UNEXPECTED(err_msg);
+    }
+  }
   std::vector<std::vector<NodeIdType>> neighbors_vec(node_list.size());
   for (size_t node_idx = 0; node_idx < node_list.size(); ++node_idx) {
+    std::shared_ptr<Node> input_node;
+    RETURN_IF_NOT_OK(GetNodeByNodeId(node_list[node_idx], &input_node));
     neighbors_vec[node_idx].emplace_back(node_list[node_idx]);
     std::vector<NodeIdType> input_list = {node_list[node_idx]};
     for (size_t i = 0; i < neighbor_nums.size(); ++i) {
@@ -204,6 +227,12 @@ Status Graph::NegativeSample(const std::vector<NodeIdType> &data, const std::uno
 Status Graph::GetNegSampledNeighbors(const std::vector<NodeIdType> &node_list, NodeIdType samples_num,
                                      NodeType neg_neighbor_type, std::shared_ptr<Tensor> *out) {
   CHECK_FAIL_RETURN_UNEXPECTED(!node_list.empty(), "Input node_list is empty.");
+  RETURN_IF_NOT_OK(CheckSamplesNum(samples_num));
+  if (node_type_map_.find(neg_neighbor_type) == node_type_map_.end()) {
+    std::string err_msg = "Invalid neighbor type:" + std::to_string(neg_neighbor_type);
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+
   std::vector<std::vector<NodeIdType>> neighbors_vec;
   neighbors_vec.resize(node_list.size());
   for (size_t node_idx = 0; node_idx < node_list.size(); ++node_idx) {
@@ -266,7 +295,7 @@ Status Graph::GetNodeFeature(const std::shared_ptr<Tensor> &nodes, const std::ve
   if (!nodes || nodes->Size() == 0) {
     RETURN_STATUS_UNEXPECTED("Input nodes is empty");
   }
-  CHECK_FAIL_RETURN_UNEXPECTED(!feature_types.empty(), "Inpude feature_types is empty");
+  CHECK_FAIL_RETURN_UNEXPECTED(!feature_types.empty(), "Input feature_types is empty");
   TensorRow tensors;
   for (const auto &f_type : feature_types) {
     std::shared_ptr<Feature> default_feature;

mindspore/ccsrc/dataset/engine/gnn/graph.h

@@ -226,6 +226,8 @@ class Graph {
   Status NegativeSample(const std::vector<NodeIdType> &input_data, const std::unordered_set<NodeIdType> &exclude_data,
                         int32_t samples_num, std::vector<NodeIdType> *out_samples);
 
+  Status CheckSamplesNum(NodeIdType samples_num);
+
   std::string dataset_file_;
   int32_t num_workers_;  // The number of worker threads
   std::mt19937 rnd_;

mindspore/ccsrc/debug/anf_ir_utils.cc

@@ -400,7 +400,7 @@ std::string AnfExporter::GetValueText(const FuncGraphPtr &func_graph, const Valu
   } else if (value->isa<tensor::Tensor>()) {
     auto tensor_ptr = dyn_cast<tensor::Tensor>(value);
     oss << value->DumpText() << "@" << DumpObject(tensor_ptr->data(), "T");
-  } else if (value->isa<parse::Symbol>() || value->isa<None>() || value->isa<NullObj>()) {
+  } else if (value->isa<parse::Symbol>() || value->isa<None>() || value->isa<Null>()) {
     oss << value->DumpText();
   } else if (value->isa<ValueSequeue>()) {
     oss << GetSequenceText(func_graph, value);

mindspore/ccsrc/device/ascend/ascend_kernel_runtime.cc

@@ -327,19 +327,16 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
   vector<std::shared_ptr<TaskInfo>> task_info_list;
   auto anf_node_list = graph->execution_order();
   TaskGenerator::GenTasks(anf_node_list, &task_info_list, graph->graph_id());
-
   // Store the task_info_list
   auto insert_ret = task_map_.insert(std::make_pair(graph->graph_id(), task_info_list));
   if (!insert_ret.second) {
     MS_LOG(EXCEPTION) << "Duplicate GraphId! Please check in ascend_session.";
   }
-
   // Graph may have no compute node, such TensorAddGrad.
   if (task_info_list.empty()) {
     MS_LOG(WARNING) << "graph " << graph->graph_id() << " have no compute node";
     return true;
   }
-
   AscendStreamAssign &assign_instance = AscendStreamAssign::GetInstance();
   AscendStreamMng &stream_manager = AscendStreamMng::GetInstance();
   AscendLabelAssign &label_assign_instance = AscendLabelAssign::GetInstance();
@@ -348,19 +345,16 @@ bool AscendKernelRuntime::GenTask(const session::KernelGraph *graph) {
   assign_instance.GetWaitStreams(&wait_active_stream_list);
   std::vector<uint32_t> force_copy_stream_list;
   assign_instance.GetHcomStreams(&force_copy_stream_list);
-
   MS_LOG(INFO) << "call DavinciModel total stream num:" << stream_manager.GetCurAllocStreamNum()
                << ", total event num:" << assign_instance.total_event_num()
                << ", total label num:" << label_assign_instance.GetLabelNum(NOT_NULL(graph))
                << ", wait_active_stream_list size:" << wait_active_stream_list.size()
                << ", force_copy_stream_list size:" << force_copy_stream_list.size();
-
   std::vector<std::shared_ptr<ge::model_runner::OpInfo>> empty_list;
   std::shared_ptr<ge::model_runner::DavinciModel> model = std::make_shared<ge::model_runner::DavinciModel>(
     task_info_list, empty_list, empty_list, empty_list, empty_list, wait_active_stream_list, force_copy_stream_list, 0,
     0, 0, 0, 0, 0, stream_manager.GetCurAllocStreamNum(), label_assign_instance.GetLabelNum(NOT_NULL(graph)),
     assign_instance.total_event_num(), 0);
-
   auto ret = graph_model_map_.insert(std::make_pair(graph->graph_id(), model));
   if (!ret.second) {
     MS_LOG(EXCEPTION) << "Duplicate GraphId! Please check in ascend_session.";

mindspore/ccsrc/device/ascend/kernel_build_ascend.cc

@@ -19,6 +19,8 @@
 #include <vector>
 #include <string>
 #include <memory>
+#include <set>
+#include <map>
 #include <functional>
 
 #include "device/ascend/kernel_select_ascend.h"
@@ -184,11 +186,17 @@ bool IsAtomicNode(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   auto kernel_mod = AnfAlgo::GetKernelMod(kernel_node);
   MS_EXCEPTION_IF_NULL(kernel_mod);
+  auto atomic_flag = false;
+  std::vector<size_t> clean_output_indexs;
+  if (AnfAlgo::HasNodeAttr(kAttrAutomicOutputIndexs, kernel_node)) {
+    clean_output_indexs = AnfAlgo::GetNodeAttr<std::vector<size_t>>(kernel_node, kAttrAutomicOutputIndexs);
+    atomic_flag = true;
+  }
+
   auto parameters_indexs = kernel_mod->GenParameters();
   if (parameters_indexs.empty()) {
-    return false;
+    return atomic_flag;
   }
-  auto atomic_flag = false;
   size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
   size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
   auto workspace_size_list = kernel_mod->GetWorkspaceSizeList();
@@ -199,7 +207,7 @@ bool IsAtomicNode(const CNodePtr &kernel_node) {
       parameters_indexs.push_back(0);
     }
   }
-  std::vector<size_t> clean_output_indexs;
+
   // in parameters data sort as input->workspace->output
   size_t index = 0;
   while (index < output_num) {
@@ -210,6 +218,8 @@ bool IsAtomicNode(const CNodePtr &kernel_node) {
     index++;
   }
   if (atomic_flag) {
+    std::set<size_t> s(clean_output_indexs.begin(), clean_output_indexs.end());
+    clean_output_indexs.assign(s.begin(), s.end());
     AnfAlgo::SetNodeAttr(kAttrAutomicOutputIndexs, MakeValue(clean_output_indexs), kernel_node);
   }
   for (size_t i = 0; i < workspace_num; ++i) {
@@ -238,11 +248,49 @@ bool KernelBuild(const mindspore::session::KernelGraph *kernel_graph_ptr) {
   return ret;
 }
 
+std::map<AnfNodePtr, std::vector<size_t>> GetCommunicationOpInputInfo(
+  const mindspore::session::KernelGraph *kernel_graph) {
+  std::map<AnfNodePtr, std::vector<size_t>> comm_input_info_map;
+  for (auto &kernel : kernel_graph->execution_order()) {
+    auto input_num = AnfAlgo::GetInputTensorNum(kernel);
+    if (mindspore::session::AnfRuntimeAlgorithm::IsCommunicationOp(kernel)) {
+      for (size_t i = 0; i < input_num; i++) {
+        auto input_node = kernel->input(i + 1);
+        auto kernel_input = AnfAlgo::VisitKernelWithReturnType(input_node, 0, true);
+        MS_LOG(INFO) << " Add atomic clean for single communication op input, comm:" << kernel->fullname_with_scope()
+                     << " input_node: " << kernel_input.first->fullname_with_scope()
+                     << " index: " << kernel_input.second;
+        auto iter = comm_input_info_map.find(kernel_input.first);
+        if (iter != comm_input_info_map.end()) {
+          iter->second.push_back(kernel_input.second);
+        } else {
+          std::vector<size_t> indexes = {kernel_input.second};
+          comm_input_info_map[kernel_input.first] = indexes;
+        }
+      }
+    }
+  }
+
+  // remove duplicate index
+  for (auto &info : comm_input_info_map) {
+    std::set<size_t> s(info.second.begin(), info.second.end());
+    info.second.assign(s.begin(), s.end());
+  }
+
+  return comm_input_info_map;
+}
+
 void KernelBuildPreprocess(mindspore::session::KernelGraph *kernel_graph) {
   MS_EXCEPTION_IF_NULL(kernel_graph);
   std::vector<CNodePtr> new_nodes;
+  std::map<AnfNodePtr, std::vector<size_t>> comm_input_info_map = GetCommunicationOpInputInfo(kernel_graph);
   for (const auto &anf_node : kernel_graph->execution_order()) {
     std::string apply_function_name = AnfAlgo::GetCNodeName(anf_node);
+    if (comm_input_info_map.find(anf_node) != comm_input_info_map.end()) {
+      auto indexes = comm_input_info_map[anf_node];
+      AnfAlgo::SetNodeAttr(kAttrAutomicOutputIndexs, MakeValue(indexes), anf_node);
+    }
+
     if (apply_function_name == prim::kPrimMaxPoolGrad->name() &&
         AnfAlgo::GetKernelType(anf_node) == KernelType::AKG_KERNEL) {
       auto clear_zero_prim = std::make_shared<Primitive>(kClearZeroOpName);

mindspore/ccsrc/device/ascend/kernel_select_ascend.cc

@@ -562,10 +562,17 @@ KernelSelectStatus SelectKernelInfo(const CNodePtr &kernel_node, KernelType kern
       MS_LOG(WARNING) << "kernel [" << (kernel_info_list.size() + index)
                       << "] :" << aicpu_kernel_info_list[index]->ToString();
     }
-    MS_LOG(WARNING) << " <<<";
-    MS_EXCEPTION(TypeError) << "The node [" << kernel_node->DebugString()
-                            << "] cannot find valid kernel info, not supported the type:" << buffer.str()
-                            << ", please refer to the supported dtypes in candidates kernel info list";
+    if (IsPrimitiveCNode(kernel_node, prim::kPrimLabelSwitch)) {
+      auto selected_kernel_info = ChooseMatchedKernelInfo(kernel_node, kernel_info_list);
+      AnfAlgo::SetSelectKernelBuildInfo(selected_kernel_info, kernel_node.get());
+      // Set format and data type for input tensor.
+      SetTensorDeviceInfo(*selected_kernel_info, kernel_node);
+    } else {
+      MS_LOG(WARNING) << " <<<";
+      MS_EXCEPTION(TypeError) << "The node [" << kernel_node->DebugString()
+                              << "] cannot find valid kernel info, not supported the type:" << buffer.str()
+                              << ", please refer to the supported dtypes in candidates kernel info list";
+    }
   }
   return select_status;
 }

mindspore/ccsrc/device/cpu/cpu_kernel_runtime.cc

@@ -147,20 +147,18 @@ BaseRef CPUKernelRuntime::CreatTensorForOutput(const session::KernelWithIndex &k
   auto &input_node = kernel_with_index.first;
   auto index = kernel_with_index.second;
   MS_EXCEPTION_IF_NULL(input_node);
-  if (input_node->isa<CNode>() && AnfAlgo::GetCNodeName(input_node) == prim::kPrimMakeTuple->name()) {
-    auto cnode = input_node->cast<CNodePtr>();
-    MS_EXCEPTION_IF_NULL(cnode);
-    VectorRef ret;
-    for (size_t i = 1; i < cnode->inputs().size(); i++) {
-      auto item_with_index = AnfAlgo::VisitKernelWithReturnType(cnode->input(i), 0);
-      auto out = CreatTensorForOutput(item_with_index, input_map, bound_addresses, need_sync_outputs);
-      ret.push_back(out);
-    }
-    return ret;
-  }
   if (input_node->isa<CNode>()) {
     auto node = input_node->cast<CNodePtr>();
     MS_EXCEPTION_IF_NULL(node);
+    if (AnfAlgo::GetCNodeName(input_node) == prim::kPrimMakeTuple->name()) {
+      VectorRef ret;
+      for (size_t i = 1; i < node->inputs().size(); i++) {
+        auto item_with_index = AnfAlgo::VisitKernelWithReturnType(node->input(i), 0);
+        auto out = CreatTensorForOutput(item_with_index, input_map, bound_addresses, need_sync_outputs);
+        ret.push_back(out);
+      }
+      return ret;
+    }
     size_t output_size = AnfAlgo::GetOutputTensorNum(node);
     if (index >= output_size) {
       MS_LOG(EXCEPTION) << "Invalid input index " << index;

mindspore/ccsrc/ir/dtype.h

@@ -275,6 +275,11 @@ extern const TypePtr kTypeExternal;
 extern const TypePtr kTypeEnv;
 extern const TypePtr kTypeType;
 extern const TypePtr kString;
+extern const TypePtr kList;
+extern const TypePtr kTuple;
+extern const TypePtr kDict;
+extern const TypePtr kSlice;
+extern const TypePtr kKeyword;
 extern const TypePtr kTensorType;
 }  // namespace mindspore
 

mindspore/ccsrc/ir/dtype/empty.cc

@@ -18,5 +18,7 @@
 
 namespace mindspore {
 const TypePtr kTypeNone = std::make_shared<TypeNone>();
+const TypePtr kTypeNull = std::make_shared<TypeNull>();
+const TypePtr kTypeEllipsis = std::make_shared<TypeEllipsis>();
 const TypePtr kAnyType = std::make_shared<TypeAnything>();
 }  // namespace mindspore

mindspore/ccsrc/ir/dtype/empty.h

@@ -71,20 +71,22 @@ class TypeNull : public Type {
 };
 using TypeNullPtr = std::shared_ptr<TypeNull>;
 
-class Ellipsis : public Type {
+class TypeEllipsis : public Type {
  public:
-  Ellipsis() : Type(kMetaTypeEllipsis) {}
-  ~Ellipsis() override {}
-  MS_DECLARE_PARENT(Ellipsis, Type)
+  TypeEllipsis() : Type(kMetaTypeEllipsis) {}
+  ~TypeEllipsis() override {}
+  MS_DECLARE_PARENT(TypeEllipsis, Type)
 
   TypeId generic_type_id() const override { return kMetaTypeEllipsis; }
-  TypePtr DeepCopy() const override { return std::make_shared<Ellipsis>(); }
+  TypePtr DeepCopy() const override { return std::make_shared<TypeEllipsis>(); }
   std::string ToReprString() const override { return "Ellipsis"; }
   std::string DumpText() const override { return "Ellipsis"; }
 };
-using EllipsisPtr = std::shared_ptr<Ellipsis>;
+using TypeEllipsisPtr = std::shared_ptr<TypeEllipsis>;
 
 extern const TypePtr kTypeNone;
+extern const TypePtr kTypeNull;
+extern const TypePtr kTypeEllipsis;
 extern const TypePtr kAnyType;
 }  // namespace mindspore
 

mindspore/ccsrc/ir/dtype_extends.cc

@@ -95,12 +95,30 @@ TypePtr TypeIdToType(TypeId id) {
       return kAnyType;
     case kMetaTypeNone:
       return kTypeNone;
+    case kMetaTypeNull:
+      return kTypeNull;
+    case kMetaTypeEllipsis:
+      return kTypeEllipsis;
     case kObjectTypeEnvType:
       return kTypeEnv;
     case kObjectTypeRefKey:
       return kRefKeyType;
     case kObjectTypeRef:
       return kRefType;
+    case kMetaTypeTypeType:
+      return kTypeType;
+    case kObjectTypeString:
+      return kString;
+    case kObjectTypeList:
+      return kList;
+    case kObjectTypeTuple:
+      return kTuple;
+    case kObjectTypeDictionary:
+      return kDict;
+    case kObjectTypeSlice:
+      return kSlice;
+    case kObjectTypeKeyword:
+      return kKeyword;
     case kTypeUnknown:
       return kTypeNone;
     default:
@@ -274,7 +292,7 @@ TypePtr StringToType(const std::string &type_name) {
   if (type_name.compare("None") == 0) {
     type = std::make_shared<TypeNone>();
   } else if (type_name.compare("Ellipsis") == 0) {
-    type = std::make_shared<Ellipsis>();
+    type = std::make_shared<TypeEllipsis>();
   } else if (type_name.compare("TypeType") == 0) {
     type = std::make_shared<TypeType>();
   } else if (type_name.compare("SymbolicKeyType") == 0) {
@@ -476,7 +494,7 @@ REGISTER_PYBIND_DEFINE(
     (void)py::class_<RefType, Type, std::shared_ptr<RefType>>(m_sub, "RefType").def(py::init());
     (void)py::class_<TypeAnything, Type, std::shared_ptr<TypeAnything>>(m_sub, "TypeAnything").def(py::init());
     (void)py::class_<Slice, Type, std::shared_ptr<Slice>>(m_sub, "Slice").def(py::init());
-    (void)py::class_<Ellipsis, Type, std::shared_ptr<Ellipsis>>(m_sub, "Ellipsis").def(py::init());
+    (void)py::class_<TypeEllipsis, Type, std::shared_ptr<TypeEllipsis>>(m_sub, "TypeEllipsis").def(py::init());
   }));
 
 const TypePtr kTypeExternal = std::make_shared<External>();
@@ -484,4 +502,9 @@ const TypePtr kTypeEnv = std::make_shared<EnvType>();
 const TypePtr kTypeType = std::make_shared<TypeType>();
 const TypePtr kTensorType = std::make_shared<TensorType>();
 const TypePtr kString = std::make_shared<String>();
+const TypePtr kList = std::make_shared<List>();
+const TypePtr kTuple = std::make_shared<Tuple>();
+const TypePtr kDict = std::make_shared<Dictionary>();
+const TypePtr kSlice = std::make_shared<Slice>();
+const TypePtr kKeyword = std::make_shared<Keyword>();
 }  // namespace mindspore

mindspore/ccsrc/ir/func_graph.cc

@@ -432,7 +432,7 @@ AnfNodePtr FuncGraph::GetDefaultValueByName(const std::string &name) {
   if (default_value == nullptr) {
     MS_LOG(EXCEPTION) << "Graph parameter " << name << " not exist";
   }
-  if (IsValueNode<NullObj>(default_value)) {
+  if (IsValueNode<Null>(default_value)) {
     return nullptr;
   }
   return default_value;
@@ -440,8 +440,8 @@ AnfNodePtr FuncGraph::GetDefaultValueByName(const std::string &name) {
 
 // set the default values
 void FuncGraph::SetDefaultValues(const std::vector<std::string> &name_list, const std::vector<AnfNodePtr> &value_list) {
-  auto all_is_null = std::all_of(value_list.begin(), value_list.end(),
-                                 [](const AnfNodePtr &node) { return IsValueNode<NullObj>(node); });
+  auto all_is_null =
+    std::all_of(value_list.begin(), value_list.end(), [](const AnfNodePtr &node) { return IsValueNode<Null>(node); });
   if (value_list.empty()) {
     all_is_null = true;
   }
@@ -457,7 +457,7 @@ void FuncGraph::ClearDefaultValues() { parameter_default_value_.clear(); }
 size_t FuncGraph::GetDefaultValueCount() {
   int null_count =
     std::count_if(parameter_default_value_.begin(), parameter_default_value_.end(),
-                  [](const std::pair<std::string, AnfNodePtr> &pair) { return IsValueNode<NullObj>(pair.second); });
+                  [](const std::pair<std::string, AnfNodePtr> &pair) { return IsValueNode<Null>(pair.second); });
   return parameter_default_value_.size() - IntToSize(null_count);
 }
 

mindspore/ccsrc/ir/named.cc

@@ -30,9 +30,9 @@ bool Named::operator==(const Value &other) const {
 abstract::AbstractBasePtr None::ToAbstract() { return std::make_shared<abstract::AbstractNone>(); }
 const NamedPtr kNone = std::make_shared<None>();
 
-abstract::AbstractBasePtr NullObj::ToAbstract() { return std::make_shared<abstract::AbstractNull>(); }
-const NamedPtr kNull = std::make_shared<NullObj>();
+abstract::AbstractBasePtr Null::ToAbstract() { return std::make_shared<abstract::AbstractNull>(); }
+const NamedPtr kNull = std::make_shared<Null>();
 
-abstract::AbstractBasePtr EllipsisObj::ToAbstract() { return std::make_shared<abstract::AbstractEllipsis>(); }
-const NamedPtr kEllipsis = std::make_shared<EllipsisObj>();
+abstract::AbstractBasePtr Ellipsis::ToAbstract() { return std::make_shared<abstract::AbstractEllipsis>(); }
+const NamedPtr kEllipsis = std::make_shared<Ellipsis>();
 }  // namespace mindspore

mindspore/ccsrc/ir/named.h

@@ -71,20 +71,20 @@ class None : public Named {
 };
 extern const NamedPtr kNone;
 
-class NullObj : public Named {
+class Null : public Named {
  public:
-  NullObj() : Named("Null") {}
-  ~NullObj() override = default;
-  MS_DECLARE_PARENT(NullObj, Named);
+  Null() : Named("Null") {}
+  ~Null() override = default;
+  MS_DECLARE_PARENT(Null, Named);
   abstract::AbstractBasePtr ToAbstract() override;
 };
 extern const NamedPtr kNull;
 
-class EllipsisObj : public Named {
+class Ellipsis : public Named {
  public:
-  EllipsisObj() : Named("Ellipsis") {}
-  ~EllipsisObj() override = default;
-  MS_DECLARE_PARENT(EllipsisObj, Named);
+  Ellipsis() : Named("Ellipsis") {}
+  ~Ellipsis() override = default;
+  MS_DECLARE_PARENT(Ellipsis, Named);
   abstract::AbstractBasePtr ToAbstract() override;
 };
 extern const NamedPtr kEllipsis;

mindspore/ccsrc/kernel/akg/akg_kernel_attrs_process.cc

@@ -68,10 +68,10 @@ void SetAkgAttrsForFive2Four(const AnfNodePtr &anf_node) {
 void SetAkgAttrsForCast(const AnfNodePtr &anf_node) {
   MS_EXCEPTION_IF_NULL(anf_node);
   // The x and output are akg op input and output param.
-  std::vector<std::string> input_names = {"x"};
+  std::vector<std::string> input_names = {"x", "dst_type"};
   std::vector<std::string> output_names = {"output"};
-  AnfAlgo::SetNodeAttr("input_names", MakeValue(input_names), anf_node);
-  AnfAlgo::SetNodeAttr("output_names", MakeValue(output_names), anf_node);
+  AnfAlgo::SetNodeAttr(kAttrInputNames, MakeValue(input_names), anf_node);
+  AnfAlgo::SetNodeAttr(kAttrOutputNames, MakeValue(output_names), anf_node);
 
   std::string dst_type;
   TypeId output_type = AnfAlgo::GetOutputDeviceDataType(anf_node, 0);

mindspore/ccsrc/kernel/akg/akg_kernel_build.cc

@@ -276,11 +276,11 @@ bool AkgKernelBuild::CreateInputDescJson(const AnfNodePtr &anf_node, nlohmann::j
       input_desc_json[kName] = op_input_name;
       input_desc_json[kTensorName] = "input_" + std::to_string(GetInputTensorIdxInc(anf_node, real_input_index));
       auto input_shape = AnfAlgo::GetInputDeviceShape(anf_node, real_input_index);
-      if (GetInputTensorValue(anf_node, real_input_index, &input_desc_json)) {
+      if (anf_node->func_graph() != nullptr && anf_node->func_graph()->has_attr(FUNC_GRAPH_ATTR_GRAPH_KERNEL) &&
+          GetInputTensorValue(anf_node, real_input_index, &input_desc_json)) {
         MS_LOG(WARNING) << "we take input[" << real_input_index << "] of [" << anf_node->DebugString(2)
                         << "] as const tensor, shape: [" << Vector2Str(input_shape)
                         << "], value: " << input_desc_json[kValue];
-
         input_shape.clear();
       }
       if (input_shape.empty()) {

mindspore/ccsrc/kernel/common_utils.cc

@@ -20,6 +20,7 @@
 #include <iostream>
 #include <utility>
 #include <fstream>
+#include <thread>
 #include "nlohmann/json.hpp"
 #include "session/anf_runtime_algorithm.h"
 #include "common/utils.h"
@@ -576,6 +577,52 @@ void DeduplicateIndexedSlices(const SparseGradient &origin_sparse_grad, SparseGr
   unique_grad->indices_size_ = unique_indices_size;
 }
 
+struct WorkerParamsForReduceSparseGradient {
+  size_t slice_start_{0};
+  size_t slice_end_{0};
+  size_t max_length_{0};
+  size_t outer_dim_{0};
+  std::vector<std::pair<int, size_t>> *sorted_indices_{nullptr};
+  std::vector<size_t> *slice_positions_{nullptr};
+  float *src_value_{nullptr};
+  SparseGradient *unique_grad_{nullptr};
+};
+
+void WorkerForReduceSparseGradient(WorkerParamsForReduceSparseGradient param) {
+  MS_EXCEPTION_IF_NULL(param.sorted_indices_);
+  MS_EXCEPTION_IF_NULL(param.slice_positions_);
+  MS_EXCEPTION_IF_NULL(param.src_value_);
+  MS_EXCEPTION_IF_NULL(param.unique_grad_);
+  auto outer_dim = param.outer_dim_;
+  auto &sorted_indices = *(param.sorted_indices_);
+  auto &slice_positions = *(param.slice_positions_);
+  auto unique_grad = param.unique_grad_;
+  for (size_t slice_id = param.slice_start_; slice_id < param.slice_end_; ++slice_id) {
+    size_t cur_pos = slice_positions[slice_id];
+    int index = sorted_indices[cur_pos].first;
+    unique_grad->indices_[slice_id] = index;
+    size_t start_index = slice_id * outer_dim;
+    auto ret_code = memcpy_s(unique_grad->value_ + start_index, (param.max_length_ - start_index) * sizeof(float),
+                             param.src_value_ + sorted_indices[cur_pos].second, outer_dim * sizeof(float));
+    if (ret_code != EOK) {
+      MS_LOG(EXCEPTION) << "Failed to copy data!";
+    }
+    cur_pos++;
+    size_t end_pos;
+    if (slice_id + 1 < slice_positions.size()) {
+      end_pos = slice_positions[slice_id + 1];
+    } else {
+      end_pos = sorted_indices.size();
+    }
+    while (cur_pos < end_pos) {
+      for (size_t i = 0; i < outer_dim; ++i) {
+        unique_grad->value_[start_index + i] += param.src_value_[sorted_indices[cur_pos].second + i];
+      }
+      cur_pos++;
+    }
+  }
+}
+
 void ReduceSparseGradient(const SparseGradient &origin_sparse_grad, SparseGradient *unique_grad, size_t first_dim,
                           size_t outer_dim) {
   MS_EXCEPTION_IF_NULL(origin_sparse_grad.value_);
@@ -583,47 +630,50 @@ void ReduceSparseGradient(const SparseGradient &origin_sparse_grad, SparseGradie
   MS_EXCEPTION_IF_NULL(unique_grad);
   MS_EXCEPTION_IF_NULL(unique_grad->value_);
   MS_EXCEPTION_IF_NULL(unique_grad->indices_);
-  size_t unique_indices_size = 0;
   std::vector<std::pair<int, size_t>> sorted_indices;
   sorted_indices.reserve(origin_sparse_grad.indices_size_);
   for (size_t i = 0; i < origin_sparse_grad.indices_size_; ++i) {
     int index = origin_sparse_grad.indices_[i];
-    if (index < 0 || IntToSize(index) >= first_dim) {
-      continue;
+    if (index >= 0 && IntToSize(index) < first_dim) {
+      sorted_indices.emplace_back(std::pair<int, size_t>(index, i * outer_dim));
     }
-    sorted_indices.emplace_back(std::pair<int, size_t>(index, i * outer_dim));
   }
   std::sort(
     sorted_indices.begin(), sorted_indices.end(),
     [](const std::pair<int, size_t> &left, const std::pair<int, size_t> &right) { return left.first < right.first; });
-
   int last_index = 0;
-  size_t indices_size = sorted_indices.size();
-  size_t start_index = 0;
-  size_t end_index = outer_dim;
-  size_t dst_len = indices_size * outer_dim;
-  for (size_t i = 0; i < indices_size; ++i) {
-    int index = sorted_indices[i].first;
-    if (i == 0 || last_index != index) {
-      if (i > 0 && last_index != index) {
-        unique_indices_size++;
-        start_index += outer_dim;
-        end_index += outer_dim;
-      }
-      unique_grad->indices_[unique_indices_size] = index;
-      auto ret_code = memcpy_s(unique_grad->value_ + start_index, dst_len - start_index,
-                               origin_sparse_grad.value_ + sorted_indices[i].second, outer_dim);
-      if (ret_code != EOK) {
-        MS_LOG(EXCEPTION) << "Failed to copy data!";
-      }
-    } else {
-      for (size_t j = start_index, k = sorted_indices[i].second; j < end_index; ++j, ++k) {
-        unique_grad->value_[j] += origin_sparse_grad.value_[k];
-      }
+  std::vector<size_t> slice_positions;
+  for (size_t i = 0; i < sorted_indices.size(); ++i) {
+    if (i == 0 || last_index != sorted_indices[i].first) {
+      slice_positions.emplace_back(i);
     }
-    last_index = index;
+    last_index = sorted_indices[i].first;
   }
-  unique_grad->indices_size_ = unique_indices_size + 1;
+  size_t thread_num = 8;
+  if (slice_positions.size() < thread_num) {
+    thread_num = slice_positions.size();
+  }
+  size_t stride = (slice_positions.size() + thread_num - 1) / thread_num;
+  thread_num = (slice_positions.size() + stride - 1) / stride;
+  std::vector<std::thread> threads;
+  size_t max_length = sorted_indices.size() * outer_dim;
+  for (size_t i = 0; i < thread_num; ++i) {
+    size_t slice_start = i * stride;
+    size_t slice_end = 0;
+    if (i == thread_num - 1) {
+      slice_end = slice_positions.size();
+    } else {
+      slice_end = slice_start + stride;
+    }
+    WorkerParamsForReduceSparseGradient params{
+      slice_start, slice_end, max_length, outer_dim, &sorted_indices, &slice_positions, origin_sparse_grad.value_,
+      unique_grad};
+    threads.emplace_back(std::thread(WorkerForReduceSparseGradient, params));
+  }
+  for (size_t i = 0; i < thread_num; ++i) {
+    threads[i].join();
+  }
+  unique_grad->indices_size_ = slice_positions.size();
 }
 
 std::pair<AnfNodePtr, size_t> GetKernelInput(const AnfNodePtr &anf_node, size_t index) {
@@ -876,5 +926,21 @@ bool IsWeightBoundary(const AnfNodePtr &node) {
   }
   return false;
 }
+
+void MultiThreadCompute(const MultiThreadComputeFunc &func, MultiThreadComputeParams *params, size_t thread_num,
+                        size_t total_compute_size) {
+  std::vector<std::thread> threads;
+  threads.reserve(thread_num);
+  size_t start = 0;
+  size_t once_compute_size = (total_compute_size + thread_num - 1) / thread_num;
+  while (start < total_compute_size) {
+    size_t end = (start + once_compute_size) > total_compute_size ? total_compute_size : (start + once_compute_size);
+    threads.emplace_back(std::thread(func, params, start, end));
+    start += once_compute_size;
+  }
+  for (size_t i = 0; i < threads.size(); ++i) {
+    threads[i].join();
+  }
+}
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/common_utils.h

@@ -25,6 +25,7 @@
 #include <string>
 #include <vector>
 #include <utility>
+#include <thread>
 #include <nlohmann/json.hpp>
 #include "kernel/kernel.h"
 #include "kernel/oplib/opinfo.h"
@@ -78,6 +79,27 @@ struct SparseGradient {
   size_t indices_size_;
 };
 
+struct MultiThreadComputeParams {
+  float *var_;
+  float *accum_;
+  float *linear_;
+  float *m_;
+  float *m_t_;
+  float *v_;
+  float lr_;
+  float l1_;
+  float l2_;
+  float lr_power_;
+  float beta1_;
+  float beta2_;
+  float epsilon_;
+  SparseGradient sparse_grad_;
+  size_t var_first_dim_size_;
+  size_t var_outer_dim_size_;
+  bool use_nesterov_;
+};
+using MultiThreadComputeFunc = std::function<void(MultiThreadComputeParams *param, size_t start, size_t end)>;
+
 bool CheckCache(const std::string &kernel_name);
 KernelPackPtr SearchCache(const std::string &kernel_name, const std::string &processor);
 KernelPackPtr InsertCache(const std::string &kernel_name, const std::string &processor);
@@ -107,6 +129,8 @@ void GetValidKernelNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr>
 bool GetInputTensorValue(const AnfNodePtr &anf_node, size_t input_idx, nlohmann::json *const node_json);
 void GetGraphRealOutput(const FuncGraphPtr &func_graph, std::vector<std::pair<AnfNodePtr, size_t>> *node_list);
 bool IsWeightBoundary(const AnfNodePtr &node);
+void MultiThreadCompute(const MultiThreadComputeFunc &func, MultiThreadComputeParams *params, size_t thread_num,
+                        size_t total_compute_size);
 }  // namespace kernel
 }  // namespace mindspore
 

mindspore/ccsrc/kernel/cpu/sparse_apply_adam_cpu_kernel.cc

@@ -14,12 +14,66 @@
  * limitations under the License.
  */
 #include "kernel/cpu/sparse_apply_adam_cpu_kernel.h"
+#include "kernel/common_utils.h"
 #include "device/cpu/cpu_device_address.h"
 
 namespace mindspore {
 namespace kernel {
 namespace {
 constexpr size_t kSparseApplyAdamInputSize = 11;
+
+void ComputeAdam(MultiThreadComputeParams *input_params, size_t start, size_t end) {
+  MS_EXCEPTION_IF_NULL(input_params);
+  auto m = input_params->m_;
+  auto m_t = input_params->m_t_;
+  auto v = input_params->v_;
+  auto beta1 = input_params->beta1_;
+  auto beta2 = input_params->beta2_;
+  auto use_nesterov = input_params->use_nesterov_;
+  auto unique_sparse_grad = input_params->sparse_grad_;
+  auto var_first_dim_size = input_params->var_first_dim_size_;
+  auto var_outer_dim_size = input_params->var_outer_dim_size_;
+  for (size_t i = start; i < end; ++i) {
+    int index = unique_sparse_grad.indices_[i];
+    if (index < 0 || IntToSize(index) >= var_first_dim_size) {
+      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
+    }
+    size_t start_index = var_outer_dim_size * index;
+    size_t end_index = start_index + var_outer_dim_size;
+    for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
+      auto summed_grad = unique_sparse_grad.value_[k];
+      m[j] += (1 - beta1) * summed_grad;
+      v[j] += (1 - beta2) * summed_grad * summed_grad;
+      if (use_nesterov) {
+        m_t[j] = m[j] * beta1 + (1 - beta1) * summed_grad;
+      }
+    }
+  }
+}
+
+void ComputeMomentum(MultiThreadComputeParams *input_params, size_t start, size_t end) {
+  MS_EXCEPTION_IF_NULL(input_params);
+  auto m = input_params->m_;
+  auto v = input_params->v_;
+  auto beta1 = input_params->beta1_;
+  auto beta2 = input_params->beta2_;
+  for (size_t i = start; i < end; ++i) {
+    m[i] *= beta1;
+    v[i] *= beta2;
+  }
+}
+
+void ComputeWeight(MultiThreadComputeParams *input_params, size_t start, size_t end) {
+  MS_EXCEPTION_IF_NULL(input_params);
+  auto var = input_params->var_;
+  auto m = input_params->m_;
+  auto v = input_params->v_;
+  auto lr = input_params->lr_;
+  auto epsilon = input_params->epsilon_;
+  for (size_t i = start; i < end; ++i) {
+    var[i] -= lr * m[i] / (std::sqrt(v[i]) + epsilon);
+  }
+}
 }  // namespace
 
 void SparseApplyAdamCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@@ -64,29 +118,6 @@ void SparseApplyAdamCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   }
 }
 
-void SparseApplyAdamCPUKernel::UpdateSparseMomentum(const SparseGradient &unique_sparse_grad, float *m, float *m_t,
-                                                    float *v, float beta1, float beta2) const {
-  MS_EXCEPTION_IF_NULL(m);
-  MS_EXCEPTION_IF_NULL(m_t);
-  MS_EXCEPTION_IF_NULL(v);
-  for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
-    int index = unique_sparse_grad.indices_[i];
-    if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
-      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
-    }
-    size_t start_index = var_outer_dim_size_ * index;
-    size_t end_index = start_index + var_outer_dim_size_;
-    for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
-      auto summed_grad = unique_sparse_grad.value_[k];
-      m[j] += (1 - beta1) * summed_grad;
-      v[j] += (1 - beta2) * summed_grad * summed_grad;
-      if (use_nesterov_) {
-        m_t[j] = m[j] * beta1 + (1 - beta1) * summed_grad;
-      }
-    }
-  }
-}
-
 bool SparseApplyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
                                       const std::vector<kernel::AddressPtr> &workspace,
                                       const std::vector<kernel::AddressPtr> & /*outputs*/) {
@@ -115,21 +146,31 @@ bool SparseApplyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inp
   ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
                        var_outer_dim_size_);
   size_t total_dim_size = var_first_dim_size_ * var_outer_dim_size_;
-  // Update momentum
   lr = lr * std::sqrt(1 - beta2_power) / (1 - beta1_power);
-  for (size_t i = 0; i < total_dim_size; ++i) {
-    m[i] *= beta1;
-    v[i] *= beta2;
-  }
+
+  MultiThreadComputeParams input_params;
+  input_params.m_ = m;
+  input_params.v_ = v;
+  input_params.beta1_ = beta1;
+  input_params.beta2_ = beta2;
+  const size_t kThreadNum = 16;
+  MultiThreadCompute(ComputeMomentum, &input_params, kThreadNum, total_dim_size);
+
   std::vector<float> m_t(m, m + total_dim_size);
-  UpdateSparseMomentum(unique_sparse_grad, m, m_t.data(), v, beta1, beta2);
-  // Update weight
+  input_params.m_t_ = m_t.data();
+  input_params.use_nesterov_ = use_nesterov_;
+  input_params.sparse_grad_ = unique_sparse_grad;
+  input_params.var_first_dim_size_ = var_first_dim_size_;
+  input_params.var_outer_dim_size_ = var_outer_dim_size_;
+  MultiThreadCompute(ComputeAdam, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
+
   if (use_nesterov_) {
-    m = m_t.data();
-  }
-  for (size_t i = 0; i < total_dim_size; ++i) {
-    var[i] -= lr * m[i] / (std::sqrt(v[i]) + epsilon);
+    input_params.m_ = input_params.m_t_;
   }
+  input_params.var_ = var;
+  input_params.lr_ = lr;
+  input_params.epsilon_ = epsilon;
+  MultiThreadCompute(ComputeWeight, &input_params, kThreadNum, total_dim_size);
   return true;
 }
 }  // namespace kernel

mindspore/ccsrc/kernel/cpu/sparse_apply_adam_cpu_kernel.h

@@ -20,7 +20,6 @@
 #include <memory>
 #include "kernel/cpu/cpu_kernel.h"
 #include "kernel/cpu/cpu_kernel_factory.h"
-#include "kernel/common_utils.h"
 
 namespace mindspore {
 namespace kernel {
@@ -35,8 +34,6 @@ class SparseApplyAdamCPUKernel : public CPUKernel {
               const std::vector<AddressPtr> &outputs) override;
 
  private:
-  void UpdateSparseMomentum(const SparseGradient &unique_sparse_grad, float *m, float *m_t, float *v, float beta1,
-                            float beta2) const;
   size_t indices_size_{0};
   size_t var_first_dim_size_{0};
   size_t var_outer_dim_size_{1};

mindspore/ccsrc/kernel/cpu/sparse_apply_ftrl_cpu_kernel.cc

@@ -21,6 +21,47 @@ namespace mindspore {
 namespace kernel {
 namespace {
 constexpr size_t kSparseApplyFtrlInputSize = 5;
+
+void ComputeFtrl(MultiThreadComputeParams *input_params, size_t start, size_t end) {
+  MS_EXCEPTION_IF_NULL(input_params);
+  auto var = input_params->var_;
+  auto accum = input_params->accum_;
+  auto linear = input_params->linear_;
+  auto lr = input_params->lr_;
+  auto l1 = input_params->l1_;
+  auto l2 = input_params->l2_;
+  auto lr_power = input_params->lr_power_;
+  auto unique_sparse_grad = input_params->sparse_grad_;
+  auto var_first_dim_size = input_params->var_first_dim_size_;
+  auto var_outer_dim_size = input_params->var_outer_dim_size_;
+  for (size_t i = start; i < end; ++i) {
+    int index = unique_sparse_grad.indices_[i];
+    if (index < 0 || IntToSize(index) >= var_first_dim_size) {
+      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
+    }
+    size_t start_index = var_outer_dim_size * index;
+    size_t end_index = start_index + var_outer_dim_size;
+    for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
+      auto summed_grad = unique_sparse_grad.value_[k];
+      auto accum_new = accum[j] + summed_grad * summed_grad;
+      if (lr_power == -0.5) {
+        linear[j] += summed_grad - (std::sqrt(accum_new) - std::sqrt(accum[j])) / lr * var[j];
+      } else {
+        linear[j] += summed_grad - (std::pow(accum_new, -lr_power) - std::pow(accum[j], -lr_power)) / lr * var[j];
+      }
+      auto x = Sign(linear[j]) * l1 - linear[j];
+      float y;
+      if (lr_power == -0.5) {
+        y = std::sqrt(accum_new) / lr + 2 * l2;
+      } else {
+        y = std::pow(accum_new, -lr_power) / lr + 2 * l2;
+      }
+      auto pre_shrink = x / y;
+      var[j] = std::fabs(linear[j]) > l1 ? pre_shrink : 0;
+      accum[j] = accum_new;
+    }
+  }
+}
 }  // namespace
 
 void SparseApplyFtrlCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@@ -96,33 +137,19 @@ bool SparseApplyFtrlCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inp
   ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
                        var_outer_dim_size_);
 
-  for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
-    int index = unique_sparse_grad.indices_[i];
-    if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
-      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
-    }
-    size_t start_index = var_outer_dim_size_ * index;
-    size_t end_index = start_index + var_outer_dim_size_;
-    for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
-      auto summed_grad = unique_sparse_grad.value_[k];
-      auto accum_new = accum[j] + summed_grad * summed_grad;
-      if (lr_power_ == -0.5) {
-        linear[j] += summed_grad - (std::sqrt(accum_new) - std::sqrt(accum[j])) / lr_ * var[j];
-      } else {
-        linear[j] += summed_grad - (std::pow(accum_new, -lr_power_) - std::pow(accum[j], -lr_power_)) / lr_ * var[j];
-      }
-      auto x = Sign(linear[j]) * l1_ - linear[j];
-      float y;
-      if (lr_power_ == -0.5) {
-        y = std::sqrt(accum_new) / lr_ + 2 * l2_;
-      } else {
-        y = std::pow(accum_new, -lr_power_) / lr_ + 2 * l2_;
-      }
-      auto pre_shrink = x / y;
-      var[j] = std::fabs(linear[j]) > l1_ ? pre_shrink : 0;
-      accum[j] = accum_new;
-    }
-  }
+  MultiThreadComputeParams input_params;
+  input_params.var_ = var;
+  input_params.accum_ = accum;
+  input_params.linear_ = linear;
+  input_params.lr_ = lr_;
+  input_params.l1_ = l1_;
+  input_params.l2_ = l2_;
+  input_params.lr_power_ = lr_power_;
+  input_params.sparse_grad_ = unique_sparse_grad;
+  input_params.var_first_dim_size_ = var_first_dim_size_;
+  input_params.var_outer_dim_size_ = var_outer_dim_size_;
+  const size_t kThreadNum = 16;
+  MultiThreadCompute(ComputeFtrl, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
   return true;
 }
 }  // namespace kernel

mindspore/ccsrc/kernel/cpu/sparse_apply_lazy_adam_cpu_kernel.cc

@@ -21,6 +21,39 @@ namespace mindspore {
 namespace kernel {
 namespace {
 constexpr size_t kSparseApplyLazyAdamInputSize = 11;
+
+void ComputeLazyAdam(MultiThreadComputeParams *input_params, size_t start, size_t end) {
+  MS_EXCEPTION_IF_NULL(input_params);
+  auto var = input_params->var_;
+  auto m = input_params->m_;
+  auto v = input_params->v_;
+  auto lr = input_params->lr_;
+  auto beta1 = input_params->beta1_;
+  auto beta2 = input_params->beta2_;
+  auto epsilon = input_params->epsilon_;
+  auto use_nesterov = input_params->use_nesterov_;
+  auto unique_sparse_grad = input_params->sparse_grad_;
+  auto var_first_dim_size = input_params->var_first_dim_size_;
+  auto var_outer_dim_size = input_params->var_outer_dim_size_;
+  for (size_t i = start; i < end; ++i) {
+    int index = unique_sparse_grad.indices_[i];
+    if (index < 0 || IntToSize(index) >= var_first_dim_size) {
+      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range";
+    }
+    size_t start_index = var_outer_dim_size * index;
+    size_t end_index = start_index + var_outer_dim_size;
+    for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
+      auto summed_grad = unique_sparse_grad.value_[k];
+      m[j] = beta1 * m[j] + (1 - beta1) * summed_grad;
+      v[j] = beta2 * v[j] + (1 - beta2) * summed_grad * summed_grad;
+      if (use_nesterov) {
+        var[j] -= lr * (m[j] * beta1 + (1 - beta1) * summed_grad) / (std::sqrt(v[j]) + epsilon);
+      } else {
+        var[j] -= lr * m[j] / (std::sqrt(v[j]) + epsilon);
+      }
+    }
+  }
+}
 }  // namespace
 
 void SparseApplyLazyAdamCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@@ -94,24 +127,20 @@ bool SparseApplyLazyAdamCPUKernel::Launch(const std::vector<kernel::AddressPtr>
                        var_outer_dim_size_);
 
   lr = lr * std::sqrt(1 - beta2_power) / (1 - beta1_power);
-  for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
-    int index = unique_sparse_grad.indices_[i];
-    if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
-      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range";
-    }
-    size_t start_index = var_outer_dim_size_ * index;
-    size_t end_index = start_index + var_outer_dim_size_;
-    for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
-      auto summed_grad = unique_sparse_grad.value_[k];
-      m[j] = beta1 * m[j] + (1 - beta1) * summed_grad;
-      v[j] = beta2 * v[j] + (1 - beta2) * summed_grad * summed_grad;
-      if (use_nesterov_) {
-        var[j] -= lr * (m[j] * beta1 + (1 - beta1) * summed_grad) / (std::sqrt(v[j]) + epsilon);
-      } else {
-        var[j] -= lr * m[j] / (std::sqrt(v[j]) + epsilon);
-      }
-    }
-  }
+  MultiThreadComputeParams input_params;
+  input_params.var_ = var;
+  input_params.m_ = m;
+  input_params.v_ = v;
+  input_params.lr_ = lr;
+  input_params.beta1_ = beta1;
+  input_params.beta2_ = beta2;
+  input_params.epsilon_ = epsilon;
+  input_params.use_nesterov_ = use_nesterov_;
+  input_params.sparse_grad_ = unique_sparse_grad;
+  input_params.var_first_dim_size_ = var_first_dim_size_;
+  input_params.var_outer_dim_size_ = var_outer_dim_size_;
+  const size_t kThreadNum = 16;
+  MultiThreadCompute(ComputeLazyAdam, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
   return true;
 }
 }  // namespace kernel

mindspore/ccsrc/kernel/cpu/sparse_apply_proximal_adagrad_cpu_kernel.cc

@@ -21,6 +21,39 @@ namespace mindspore {
 namespace kernel {
 namespace {
 constexpr size_t kSparseApplyProximalAdagradInputSize = 7;
+
+void ComputeProximalAdagrad(MultiThreadComputeParams *input_params, size_t start, size_t end) {
+  MS_EXCEPTION_IF_NULL(input_params);
+  auto var = input_params->var_;
+  auto accum = input_params->accum_;
+  auto lr = input_params->lr_;
+  auto l1 = input_params->l1_;
+  auto l2 = input_params->l2_;
+  auto unique_sparse_grad = input_params->sparse_grad_;
+  auto var_first_dim_size = input_params->var_first_dim_size_;
+  auto var_outer_dim_size = input_params->var_outer_dim_size_;
+  for (size_t i = start; i < end; ++i) {
+    int index = unique_sparse_grad.indices_[i];
+    if (index < 0 || IntToSize(index) >= var_first_dim_size) {
+      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
+    }
+    size_t start_index = var_outer_dim_size * index;
+    size_t end_index = start_index + var_outer_dim_size;
+    for (size_t j = start_index, k = var_outer_dim_size * i; j < end_index; ++j, ++k) {
+      auto summed_grad = unique_sparse_grad.value_[k];
+      accum[j] += summed_grad * summed_grad;
+      auto learning_rate = lr * (1 / std::sqrt(accum[j]));
+      auto prox_v = var[j];
+      prox_v -= summed_grad * learning_rate;
+      if (l1 > 0) {
+        var[j] = Sign(prox_v) * std::fmax(std::fabs(prox_v) - learning_rate * l1, static_cast<float>(0.0)) /
+                 (1 + l2 * learning_rate);
+      } else {
+        var[j] = prox_v / (1 + l2 * learning_rate);
+      }
+    }
+  }
+}
 }  // namespace
 
 void SparseApplyProximalAdagradCPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
@@ -90,27 +123,17 @@ bool SparseApplyProximalAdagradCPUKernel::Launch(const std::vector<kernel::Addre
   ReduceSparseGradient(SparseGradient({grad, indices, indices_size_}), &unique_sparse_grad, var_first_dim_size_,
                        var_outer_dim_size_);
 
-  for (size_t i = 0; i < unique_sparse_grad.indices_size_; ++i) {
-    int index = unique_sparse_grad.indices_[i];
-    if (index < 0 || IntToSize(index) >= var_first_dim_size_) {
-      MS_LOG(EXCEPTION) << "Index " << index << " in indices is out of range after unique process";
-    }
-    size_t start_index = var_outer_dim_size_ * index;
-    size_t end_index = start_index + var_outer_dim_size_;
-    for (size_t j = start_index, k = var_outer_dim_size_ * i; j < end_index; ++j, ++k) {
-      auto summed_grad = unique_sparse_grad.value_[k];
-      accum[j] += summed_grad * summed_grad;
-      auto learning_rate = lr * (1 / std::sqrt(accum[j]));
-      auto prox_v = var[j];
-      prox_v -= summed_grad * learning_rate;
-      if (l1 > 0) {
-        var[j] = Sign(prox_v) * std::fmax(std::fabs(prox_v) - learning_rate * l1, static_cast<float>(0.0)) /
-                 (1 + l2 * learning_rate);
-      } else {
-        var[j] = prox_v / (1 + l2 * learning_rate);
-      }
-    }
-  }
+  MultiThreadComputeParams input_params;
+  input_params.var_ = var;
+  input_params.accum_ = accum;
+  input_params.lr_ = lr;
+  input_params.l1_ = l1;
+  input_params.l2_ = l2;
+  input_params.sparse_grad_ = unique_sparse_grad;
+  input_params.var_first_dim_size_ = var_first_dim_size_;
+  input_params.var_outer_dim_size_ = var_outer_dim_size_;
+  const size_t kThreadNum = 16;
+  MultiThreadCompute(ComputeProximalAdagrad, &input_params, kThreadNum, unique_sparse_grad.indices_size_);
   return true;
 }
 }  // namespace kernel

mindspore/ccsrc/kernel/cpu/sparse_apply_proximal_adagrad_cpu_kernel.h

@@ -48,6 +48,7 @@ MS_REG_CPU_KERNEL(SparseApplyProximalAdagrad,
                     .AddInputAttr(kNumberTypeFloat32)
                     .AddInputAttr(kNumberTypeFloat32)
                     .AddInputAttr(kNumberTypeInt32)
+                    .AddOutputAttr(kNumberTypeFloat32)
                     .AddOutputAttr(kNumberTypeFloat32),
                   SparseApplyProximalAdagradCPUKernel);
 }  // namespace kernel

mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_perchannel_impl.cu

@@ -0,0 +1,138 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include <thrust/extrema.h>
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/reduce.h>
+#include <thrust/pair.h>
+#include "fake_quant_perchannel_impl.cuh"
+
+/**
+ * Find the nudge min, max and scale value as output.
+ * @param input_min array
+ * @param input_max array
+ * @param quant_min 1 << bit -1
+ * @param quant_max 0
+ * @param nudge_min array
+ * @param nudge_max array
+ * @param scale array
+ * @param channel_num
+ * @return
+ */
+__global__ void NudgeMinMaxPerChannel(float *input_min, float *input_max, const float quant_min, const float quant_max,
+                                      float *nudge_min, float *nudge_max, float *scale, int channel_num,
+                                      const bool symmetric) {
+  float zp_from_min = 0.f;
+  float nudge_zp = 0.f;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < channel_num; i += blockDim.x * gridDim.x) {
+    if (symmetric) {
+      input_max[i] = abs(input_min[0]) < input_max[i] ? input_max[i] : -input_min[i];
+      input_min[i] = abs(input_min[i]) < input_max[i] ? -input_max[i] : input_min[i];
+    }
+    if ((quant_max - quant_min) == 0 || (input_max[i] - input_min[i]) == 0) {
+      scale[i] = 0.f;
+      zp_from_min = 0.f;
+    } else {
+      scale[i] = (input_max[i] - input_min[i]) / (quant_max - quant_min);
+      zp_from_min = quant_min - input_min[i] / scale[i];
+    }
+
+    if (zp_from_min <= quant_min) {
+      nudge_zp = quant_min;
+    } else if (zp_from_min >= quant_max) {
+      nudge_zp = quant_max;
+    } else {
+      nudge_zp = round(zp_from_min);
+    }
+
+    nudge_min[i] = (quant_min - nudge_zp) * (scale[i]);
+    nudge_max[i] = (quant_max - nudge_zp) * (scale[i]);
+  }
+}
+
+void CalNudgePerChannel(float *input_min, float *input_max, const float quant_min, const float quant_max,
+                        float *nudge_min, float *nudge_max, float *scale, const int channel_num, const bool symmetric,
+                        cudaStream_t cuda_stream) {
+  NudgeMinMaxPerChannel<<<GET_BLOCKS(channel_num), GET_THREADS, 0, cuda_stream>>>(
+    input_min, input_max, quant_min, quant_max, nudge_min, nudge_max, scale, channel_num, symmetric);
+}
+
+/**
+ * Calulate fake quant output accroding by nudge min, nudge max, nudge scale.
+ * @param input - array
+ * @param output - array
+ * @param total_size - int, purpose for cal the per chanel number in filters
+ * @param channel_size - int, purpose for cal the per channel number in filters
+ * @param nudge_min - array
+ * @param nudge_max - array
+ * @param scale - array
+ * @return
+ */
+__global__ void FakeQuantPerChannel(const float *input, float *output, const int total_size, const int channel_size,
+                                    const float *nudge_min, const float *nudge_max, const float *scale) {
+  float input_x = 0.f;
+  int nudge_input = 0;
+  int channel_idx = 0;
+  int per_channel_num = total_size / channel_size;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < total_size; i += blockDim.x * gridDim.x) {
+    input_x = input[i];
+    channel_idx = floor(static_cast<double>(i) / static_cast<double>(per_channel_num));
+    // clamp input x
+    if (input_x < nudge_min[channel_idx]) {
+      input_x = nudge_min[channel_idx];
+    }
+    if (input_x > nudge_max[channel_idx]) {
+      input_x = nudge_max[channel_idx];
+    }
+    // clamp shift
+    nudge_input = floor((input_x - nudge_min[channel_idx]) / scale[channel_idx] + 0.5f);
+
+    // quantize
+    output[i] = nudge_input * scale[channel_idx] + nudge_min[channel_idx];
+  }
+}
+
+void CalFakeQuantPerChannel(const float *input, float *output, const int total_size, const int channel_size,
+                            const float *nudge_min, const float *nudge_max, const float *scale,
+                            cudaStream_t cuda_stream) {
+  FakeQuantPerChannel<<<GET_BLOCKS(total_size), GET_THREADS, 0, cuda_stream>>>(input, output, total_size, channel_size,
+                                                                               nudge_min, nudge_max, scale);
+}
+
+__global__ void FakeQuantPerChannelGrad(const float *input, const float *gradient, float *output, const int total_size,
+                                        const int channel_size, const float *nudge_min, const float *nudge_max) {
+  int channel_idx = 0;
+  int per_channel_num = total_size / channel_size;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < total_size; i += blockDim.x * gridDim.x) {
+    channel_idx = floor(static_cast<double>(i) / static_cast<double>(per_channel_num));
+    if (input[i] < nudge_min[channel_idx] || input[i] > nudge_max[channel_idx]) {
+      output[i] = 0;
+    } else {
+      output[i] = gradient[i];
+    }
+  }
+}
+
+void CalFakeQuantPerChannelGrad(const float *input, const float *gradient, float *output, const int total_num,
+                                const int channel_num, const float *nudge_min, const float *nudge_max,
+                                cudaStream_t cuda_stream) {
+  FakeQuantPerChannelGrad<<<GET_BLOCKS(channel_num), GET_THREADS, 0, cuda_stream>>>(input, gradient, output, total_num,
+                                                                                    channel_num, nudge_min, nudge_max);
+}

mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_perchannel_impl.cuh

@@ -0,0 +1,34 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKE_QUANT_PERCHANNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKE_QUANT_PERCHANNEL_H_
+
+#include "device/gpu/cuda_common.h"
+
+void CalNudgePerChannel(float *input_min, float *input_max, const float quant_min, const float quant_max,
+                        float *nudge_min, float *nudge_max, float *scale, const int channel_num, const bool symmetric,
+                        cudaStream_t cuda_stream);
+
+void CalFakeQuantPerChannel(const float *input, float *output, const int total_num, const int channel_num,
+                            const float *nudge_min, const float *nudge_max, const float *scale,
+                            cudaStream_t cuda_stream);
+
+void CalFakeQuantPerChannelGrad(const float *input, const float *gradient, float *output, const int total_num,
+                                const int channel_num, const float *nudge_min, const float *nudge_max,
+                                cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKE_QUANT_PERCHANNEL_H_

mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_perlayer_impl.cu

@@ -0,0 +1,111 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include <thrust/extrema.h>
+#include <thrust/device_vector.h>
+#include <thrust/pair.h>
+#include "fake_quant_perlayer_impl.cuh"
+
+__global__ void FakeQuantPerLayer(const float *input, float *output, const int size, const float *nudge_min,
+                                  const float *nudge_max, const float *scale) {
+  float input_x = 0.f;
+  int nudge_input = 0;
+
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += blockDim.x * gridDim.x) {
+    input_x = input[i];
+    // clamp input x
+    if (input_x < nudge_min[0]) {
+      input_x = nudge_min[0];
+    }
+    if (input_x > nudge_max[0]) {
+      input_x = nudge_max[0];
+    }
+    // clamp shift
+    nudge_input = round((input_x - nudge_min[0]) / scale[0]);
+
+    // quantize
+    output[i] = nudge_input * scale[0] + nudge_min[0];
+  }
+  return;
+}
+
+__global__ void FakeQuantPerLayerGrad(const float *input, const float *gradient, float *output, const int size,
+                                      const float *nudge_min, const float *nudge_max) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += blockDim.x * gridDim.x) {
+    if (input[i] < nudge_min[0] || input[i] > nudge_max[0]) {
+      output[i] = 0;
+    } else {
+      output[i] = gradient[i];
+    }
+  }
+  return;
+}
+
+__global__ void NudgeMinMaxPerLayer(float *input_min, float *input_max, const float quant_min, const float quant_max,
+                                    float *nudge_min, float *nudge_max, float *scale, const bool symmetric) {
+  float zp_from_min = 0.f;
+  scale[0] = 0.f;
+  nudge_max[0] = 0.f;
+  nudge_min[0] = 0.f;
+
+  if (symmetric) {
+    input_max[0] = abs(input_min[0]) < input_max[0] ? input_max[0] : -input_min[0];
+    input_min[0] = abs(input_min[0]) < input_max[0] ? -input_max[0] : input_min[0];
+  }
+
+  if ((quant_max - quant_min) == 0 || (input_max[0] - input_min[0]) == 0) {
+    scale[0] = 0.f;
+    zp_from_min = 0.f;
+  } else {
+    scale[0] = (input_max[0] - input_min[0]) / (quant_max - quant_min);
+    zp_from_min = quant_min - input_min[0] / scale[0];
+  }
+
+  float nudge_zp = 0.f;
+  if (zp_from_min <= quant_min) {
+    nudge_zp = quant_min;
+  } else if (zp_from_min >= quant_max) {
+    nudge_zp = quant_max;
+  } else {
+    nudge_zp = round(zp_from_min);
+  }
+
+  nudge_min[0] = (quant_min - nudge_zp) * (scale[0]);
+  nudge_max[0] = (quant_max - nudge_zp) * (scale[0]);
+  return;
+}
+
+void CalFakeQuantPerLayer(const float *input, float *output, const int size, const float *nudge_min,
+                          const float *nudge_max, const float *scale, cudaStream_t cuda_stream) {
+  FakeQuantPerLayer<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(input, output, size, nudge_min, nudge_max,
+                                                                       scale);
+  return;
+}
+
+void CalFakeQuantPerLayerGrad(const float *input, const float *gradient, float *output, const int size,
+                              const float *nudge_min, const float *nudge_max, cudaStream_t cuda_stream) {
+  FakeQuantPerLayerGrad<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(input, gradient, output, size, nudge_min,
+                                                                           nudge_max);
+  return;
+}
+
+void CalNudgePerLayer(float *input_min, float *input_max, const float quant_min, const float quant_max,
+                      float *nudge_min, float *nudge_max, float *scale, const bool symmetric,
+                      cudaStream_t cuda_stream) {
+  NudgeMinMaxPerLayer<<<1, 1, 0, cuda_stream>>>(input_min, input_max, quant_min, quant_max, nudge_min, nudge_max, scale,
+                                                symmetric);
+  return;
+}

mindspore/ccsrc/kernel/gpu/cuda_impl/fake_quant_perlayer_impl.cuh

@@ -0,0 +1,31 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKE_QUANT_PERLAYER_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKE_QUANT_PERLAYER_H_
+
+#include "device/gpu/cuda_common.h"
+
+void CalNudgePerLayer(float *input_min, float *input_max, const float quant_min, const float quant_max,
+                      float *nudge_min, float *nudge_max, float *scale, const bool symmetric, cudaStream_t cuda_stream);
+
+void CalFakeQuantPerLayer(const float *input, float *output, const int size, const float *nudge_min,
+                          const float *nudge_max, const float *scale, cudaStream_t cuda_stream);
+
+void CalFakeQuantPerLayerGrad(const float *input, const float *gradient, float *output, const int size,
+                              const float *nudge_min, const float *nudge_max, cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_FAKE_QUANT_PERLAYER_H_

mindspore/ccsrc/kernel/gpu/cuda_impl/minmax_update_impl.cu

@@ -0,0 +1,87 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include <thrust/extrema.h>
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/reduce.h>
+#include <thrust/pair.h>
+#include "minmax_update_impl.cuh"
+#include "device/gpu/cuda_common.h"
+
+__global__ void UpdateInputMinMaxPerLayerWithEMA(const float *input_min, const float *input_max, float *output_min,
+                                                 float *output_max, const float min, const float max,
+                                                 const float decay) {
+  output_min[0] = decay * (min) + (1 - decay) * (input_min[0]);
+  output_min[0] = input_min[0] > 0 ? 0 : input_min[0];
+  output_max[0] = decay * (max) + (1 - decay) * (input_max[0]);
+  output_max[0] = input_max[0] < 0 ? 0 : input_max[0];
+  return;
+}
+
+__global__ void UpdateInputMinMaxPerLayer(float *output_min, float *output_max, const float min, const float max) {
+  output_min[0] = min > 0 ? 0 : min;
+  output_max[0] = max < 0 ? 0 : max;
+  return;
+}
+
+__global__ void UpdateInputMinMaxPerChannel(float *input, float *input_min, float *input_max, float *output_min,
+                                            float *output_max, int channels, int per_channel_nums, bool ema,
+                                            float ema_decay) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < channels; i += blockDim.x * gridDim.x) {
+    thrust::pair<float *, float *> sum =
+      thrust::minmax_element(thrust::device, input + i * per_channel_nums, input + per_channel_nums * (i + 1));
+    if (ema) {
+      output_min[i] = ema_decay * sum.first[0] + (1 - ema_decay) * input_min[i];
+      output_max[i] = ema_decay * sum.second[0] + (1 - ema_decay) * input_max[i];
+    } else {
+      output_min[i] = sum.first[0];
+      output_max[i] = sum.second[0];
+    }
+    output_min[i] = input_min[i] > 0 ? 0 : input_min[i];
+    output_max[i] = input_max[i] < 0 ? 0 : input_max[i];
+  }
+  return;
+}
+
+void CalMinMaxPerChannel(float *input, float *input_min, float *input_max, float *output_min, float *output_max,
+                         const int total_num, const int channel_num, const float ema_decay, const bool ema,
+                         cudaStream_t cuda_stream) {
+  int per_channel_num = total_num / channel_num;
+  UpdateInputMinMaxPerChannel<<<GET_BLOCKS(channel_num), GET_THREADS, 0, cuda_stream>>>(
+    input, input_min, input_max, output_min, output_max, channel_num, per_channel_num, ema, ema_decay);
+  return;
+}
+
+void CalMinMaxPerLayer(float *input, float *input_min, float *input_max, float *output_min, float *output_max,
+                       const int total_num, const float ema_decay, const bool ema, cudaStream_t cuda_stream) {
+  float minel = 0.f;
+  float maxel = 0.f;
+  auto policy = thrust::cuda::par.on(cuda_stream);
+  thrust::pair<thrust::device_ptr<float>, thrust::device_ptr<float>> tuple;
+  tuple =
+    thrust::minmax_element(policy, thrust::device_pointer_cast(input), thrust::device_pointer_cast(input) + total_num);
+  minel = tuple.first[0];
+  maxel = tuple.second[0];
+
+  if (ema) {
+    UpdateInputMinMaxPerLayerWithEMA<<<1, 1, 0, cuda_stream>>>(input_min, input_max, output_min, output_max, minel,
+                                                               maxel, ema_decay);
+  } else {
+    UpdateInputMinMaxPerLayer<<<1, 1, 0, cuda_stream>>>(output_min, output_max, minel, maxel);
+  }
+  return;
+}

mindspore/ccsrc/kernel/gpu/cuda_impl/minmax_update_impl.cuh

@@ -0,0 +1,29 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_MIN_MAX_UPDATE_IMPL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_MIN_MAX_UPDATE_IMPL_H_
+
+#include "device/gpu/cuda_common.h"
+
+void CalMinMaxPerChannel(float *input, float *input_min, float *input_max, float *output_min, float *output_max,
+                         const int total_num, const int channel_num, const float ema_decay, const bool ema,
+                         cudaStream_t cuda_stream);
+
+void CalMinMaxPerLayer(float *input, float *input_min, float *input_max, float *output_min, float *output_max,
+                       const int size, const float ema_decay, const bool ema, cudaStream_t cuda_stream);
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_MIN_MAX_UPDATE_IMPL_H_

mindspore/ccsrc/kernel/gpu/math/broadcast_gpu_kernel.cc

@@ -96,5 +96,8 @@ MS_REG_GPU_KERNEL_TWO(
 MS_REG_GPU_KERNEL_TWO(
   Maximum, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
   BroadcastOpGpuKernel, int, int)
+MS_REG_GPU_KERNEL_TWO(
+  Mul, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  BroadcastOpGpuKernel, int, int)
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/fake_quant_gpu_kernel.cc

@@ -1,176 +0,0 @@
-/**
- * Copyright 2020 Huawei Technologies Co., Ltd
- *
- * 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.
- */
-
-#include "kernel/gpu/quant/fake_quant_gpu_kernel.h"
-#include "kernel/gpu/cuda_impl/fake_quant_impl.cuh"
-#include <thrust/extrema.h>
-#include <thrust/pair.h>
-#include <thrust/device_vector.h>
-#include <cuda_runtime_api.h>
-
-namespace mindspore {
-namespace kernel {
-FakeQuantGpuKernel::FakeQuantGpuKernel()
-    : input_size_(0),
-      min_size_(0),
-      max_size_(0),
-      output_size_(0),
-      workspace_size_(0),
-      num_bits_(0),
-      quant_min_(0),
-      quant_max_(0),
-      quant_num_(0),
-      quant_delay_(0),
-      ema_(false),
-      ema_decay_(0),
-      global_step_(0),
-      training_(false),
-      narrow_range_(false),
-      symmetric_(false) {}
-
-const std::vector<size_t> &FakeQuantGpuKernel::GetInputSizeList() const { return input_size_list_; }
-
-const std::vector<size_t> &FakeQuantGpuKernel::GetOutputSizeList() const { return output_size_list_; }
-
-const std::vector<size_t> &FakeQuantGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
-
-bool FakeQuantGpuKernel::Init(const CNodePtr &kernel_node) {
-  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
-  if (input_num != 3) {
-    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuant GpuKernel OP needs 3 output.";
-  }
-
-  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
-  if (output_num != 1) {
-    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuant GpuKernel OP needs 1 output.";
-  }
-
-  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
-  ema_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema"));
-  ema_decay_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema_decay"));
-  training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training"));
-
-  if (num_bits_ <= 2 || num_bits_ >= 16) {
-    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
-  }
-
-  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
-  if (quant_delay_ < 0) {
-    MS_LOG(EXCEPTION) << "Attr \'quant_delay\' " << num_bits_ << "is less then 0, require larger than 0.";
-  }
-
-  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
-  if (symmetric_) {
-    quant_min_ = 0 - (1 << (num_bits_ - 1));
-    quant_max_ = (1 << (num_bits_ - 1)) - 1;
-  } else {
-    quant_min_ = 0;
-    quant_max_ = (1 << num_bits_) - 1;
-  }
-
-  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
-  if (narrow_range_) {
-    quant_min_++;
-  }
-
-  if (quant_num_ == 0) {
-    quant_num_ = 1;
-  }
-  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  for (size_t i = 0; i < input_shape.size(); ++i) {
-    quant_num_ *= SizeToInt(input_shape[i]);
-  }
-
-  input_size_ = sizeof(float);
-  min_size_ = sizeof(float);
-  max_size_ = sizeof(float);
-  for (size_t i = 0; i < input_shape.size(); i++) {
-    input_size_ *= input_shape[i];
-  }
-  output_size_ = input_size_;
-  InitSizeLists();
-  return true;
-}
-
-void FakeQuantGpuKernel::InitSizeLists() {
-  input_size_list_.push_back(input_size_);  // input
-  input_size_list_.push_back(min_size_);    // min
-  input_size_list_.push_back(max_size_);    // max
-  output_size_list_.push_back(output_size_);
-  workspace_size_list_.push_back(workspace_size_);
-}
-
-bool FakeQuantGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
-                                const std::vector<AddressPtr> &outputs, void *stream_ptr) {
-  float *output = GetDeviceAddress<float>(outputs, 0);
-  float *input = GetDeviceAddress<float>(inputs, 0);
-  float *input_min = GetDeviceAddress<float>(inputs, 1);
-  float *input_max = GetDeviceAddress<float>(inputs, 2);
-
-  if (input == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGpuKernel input x is null.";
-  }
-  if (input_min == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGpuKernel input min is null.";
-  }
-  if (input_max == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGpuKernel input max is null.";
-  }
-
-  // Allocate space for device copies
-  int size = sizeof(float);
-  float *d_scale = nullptr;
-  float *d_nudge_min = nullptr;
-  float *d_nudge_max = nullptr;
-  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), size), "Malloc gpu memory failed");
-  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), size), "Malloc gpu memory failed");
-  CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), size), "Malloc gpu memory failed");
-
-  if (training_) {
-    // calculate the input min and max according by the parameter ema and ema_decay.
-    CalMinMax(input, input_min, input_max, quant_num_, ema_decay_, ema_, reinterpret_cast<cudaStream_t>(stream_ptr));
-    // control flow for quant_delay
-    if (global_step_ >= quant_delay_) {
-      // real launch
-      CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
-               reinterpret_cast<cudaStream_t>(stream_ptr));
-      CalFakeQuantize(input, output, quant_num_, d_nudge_min, d_nudge_max, d_scale, symmetric_,
-                      reinterpret_cast<cudaStream_t>(stream_ptr));
-    } else {
-      CHECK_CUDA_RET_WITH_ERROR(cudaMemcpyAsync(output, input, input_size_, cudaMemcpyDeviceToDevice,
-                                                reinterpret_cast<cudaStream_t>(stream_ptr)),
-                                "Copy gpu memory failed");
-    }
-    global_step_++;
-  } else {
-    // real launch
-    CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
-             reinterpret_cast<cudaStream_t>(stream_ptr));
-    CalFakeQuantize(input, output, quant_num_, d_nudge_min, d_nudge_max, d_scale, symmetric_,
-                    reinterpret_cast<cudaStream_t>(stream_ptr));
-  }
-
-  // Cleanup
-  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
-  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
-  CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
-
-  return true;
-}
-
-MS_REG_GPU_KERNEL(FakeQuantPerLayer, FakeQuantGpuKernel)
-}  // namespace kernel
-}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/fake_quant_grad_gpu_kernel.cc

@@ -1,157 +0,0 @@
-/**
- * Copyright 2020 Huawei Technologies Co., Ltd
- *
- * 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.
- */
-
-#include "kernel/gpu/quant/fake_quant_grad_gpu_kernel.h"
-#include "kernel/gpu/cuda_impl/fake_quant_impl.cuh"
-
-namespace mindspore {
-namespace kernel {
-FakeQuantGradGpuKernel::FakeQuantGradGpuKernel()
-    : input_size_(0),
-      min_size_(0),
-      max_size_(0),
-      output_size_(0),
-      workspace_size_(0),
-      num_bits_(0),
-      quant_min_(0),
-      quant_max_(0),
-      quant_size_(0),
-      quant_delay_(0),
-      global_step_(0),
-      narrow_range_(false),
-      symmetric_(false) {}
-
-const std::vector<size_t> &FakeQuantGradGpuKernel::GetInputSizeList() const { return input_size_list_; }
-
-const std::vector<size_t> &FakeQuantGradGpuKernel::GetOutputSizeList() const { return output_size_list_; }
-
-const std::vector<size_t> &FakeQuantGradGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
-
-bool FakeQuantGradGpuKernel::Init(const CNodePtr &kernel_node) {
-  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
-  if (input_num != 4) {
-    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuantGrad GpuKernel OP needs 4 output.";
-  }
-
-  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
-  if (output_num != 1) {
-    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuantGrad GpuKernel OP needs 1 output.";
-  }
-
-  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
-  if (num_bits_ <= 2 || num_bits_ >= 16) {
-    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
-  }
-
-  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
-  if (quant_delay_ < 0) {
-    MS_LOG(EXCEPTION) << "Attr \'quant_delay_\' " << quant_delay_ << " is less then 0, require larger than 0.";
-  }
-
-  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
-  if (symmetric_) {
-    quant_min_ = 0 - (1 << (num_bits_ - 1));
-    quant_max_ = (1 << (num_bits_ - 1)) - 1;
-  } else {
-    quant_min_ = 0;
-    quant_max_ = (1 << num_bits_) - 1;
-  }
-
-  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
-  if (narrow_range_) {
-    quant_min_++;
-  }
-
-  if (quant_size_ == 0) {
-    quant_size_ = 1;
-  }
-  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  for (size_t i = 0; i < input_shape.size(); ++i) {
-    quant_size_ *= SizeToInt(input_shape[i]);
-  }
-
-  input_size_ = sizeof(float);
-  min_size_ = sizeof(float);
-  max_size_ = sizeof(float);
-  for (size_t i = 0; i < input_shape.size(); i++) {
-    input_size_ *= input_shape[i];
-  }
-  output_size_ = input_size_;
-
-  InitSizeLists();
-  return true;
-}
-
-void FakeQuantGradGpuKernel::InitSizeLists() {
-  input_size_list_.push_back(input_size_);  // gradient
-  input_size_list_.push_back(input_size_);  // input
-  input_size_list_.push_back(min_size_);    // min
-  input_size_list_.push_back(max_size_);    // max
-  output_size_list_.push_back(output_size_);
-}
-
-bool FakeQuantGradGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
-                                    const std::vector<AddressPtr> &outputs, void *stream_ptr) {
-  float *output = GetDeviceAddress<float>(outputs, 0);
-  float *gradient = GetDeviceAddress<float>(inputs, 0);
-  float *input = GetDeviceAddress<float>(inputs, 1);
-  float *input_min = GetDeviceAddress<float>(inputs, 2);
-  float *input_max = GetDeviceAddress<float>(inputs, 3);
-
-  if (gradient == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel gradient is null";
-  }
-  if (input == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input is null.";
-  }
-  if (input_min == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input min is null.";
-  }
-  if (input_max == nullptr) {
-    MS_LOG(EXCEPTION) << "FakeQuantGradGpuKernel input max is null.";
-  }
-
-  if (global_step_ >= quant_delay_) {
-    float *d_scale = nullptr;
-    float *d_nudge_min = nullptr;
-    float *d_nudge_max = nullptr;
-    int size = sizeof(float);
-    // Allocate space for device copies
-    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_scale), size), "Malloc gpu memory failed");
-    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_min), size), "Malloc gpu memory failed");
-    CHECK_CUDA_RET_WITH_ERROR(cudaMalloc(reinterpret_cast<void **>(&d_nudge_max), size), "Malloc gpu memory failed");
-
-    CalNudge(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale,
-             reinterpret_cast<cudaStream_t>(stream_ptr));
-    CalFakeQuantizeGrad(input, gradient, output, quant_size_, d_nudge_min, d_nudge_max,
-                        reinterpret_cast<cudaStream_t>(stream_ptr));
-
-    // Cleanup
-    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_scale), "Free gpu memory failed");
-    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_min), "Free gpu memory failed");
-    CHECK_CUDA_RET_WITH_ERROR(cudaFree(d_nudge_max), "Free gpu memory failed");
-  } else {
-    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpyAsync(output, gradient, input_size_, cudaMemcpyDeviceToDevice,
-                                              reinterpret_cast<cudaStream_t>(stream_ptr)),
-                              "Copy gpu memory failed");
-  }
-  global_step_++;
-  return true;
-}
-
-MS_REG_GPU_KERNEL(FakeQuantPerLayerGrad, FakeQuantGradGpuKernel)
-}  // namespace kernel
-}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perchannel_gpu_kernel.cc

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#include "kernel/gpu/quant/fake_quant_per_channel_gpu_kernel.h"
-#include "kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh"
+#include "kernel/gpu/quant/fake_quant_perchannel_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_perchannel_impl.cuh"
 #include <thrust/extrema.h>
 #include <thrust/pair.h>
 #include <thrust/device_vector.h>
@@ -25,21 +25,15 @@ namespace mindspore {
 namespace kernel {
 FakeQuantPerChannelGpuKernel::FakeQuantPerChannelGpuKernel()
     : input_size_(0),
-      min_size_(0),
-      max_size_(0),
-      output_size_(0),
-      workspace_size_(0),
+      num_channels_(0),
       num_bits_(0),
+      training_(false),
+      symmetric_(false),
+      narrow_range_(false),
+      quant_delay_(0),
       quant_min_(0),
       quant_max_(0),
-      quant_delay_(0),
-      ema_(false),
-      ema_decay_(0),
-      global_step_(0),
-      training_(false),
-      channel_out_(0),
-      narrow_range_(false),
-      symmetric_(false) {}
+      global_step_(0) {}
 
 const std::vector<size_t> &FakeQuantPerChannelGpuKernel::GetInputSizeList() const { return input_size_list_; }
 
@@ -60,91 +54,57 @@ bool FakeQuantPerChannelGpuKernel::Init(const CNodePtr &kernel_node) {
     return false;
   }
 
+  // get attribute
   num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
-  ema_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema"));
-  ema_decay_ = 1.0 - GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema_decay"));
+  training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training"));
+  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
+  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
 
   if (num_bits_ <= 2 || num_bits_ >= 16) {
     MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << "is out of range, expected between 2 and 16.";
     return false;
   }
 
-  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
   if (quant_delay_ < 0) {
     MS_LOG(EXCEPTION) << "Attr \'quant_delay\' " << num_bits_ << " is less then 0, require larger than 0.";
     return false;
   }
 
-  training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training"));
-
-  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
-  if (symmetric_) {
-    quant_min_ = 0 - (1 << (num_bits_ - 1));
-    quant_max_ = (1 << (num_bits_ - 1)) - 1;
-  } else {
-    quant_min_ = 0;
-    quant_max_ = (1 << num_bits_) - 1;
-  }
-
-  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+  // quant min and max value
+  quant_min_ = 0;
+  quant_max_ = (1 << num_bits_) - 1;
   if (narrow_range_) {
     quant_min_++;
   }
 
   // shape info for gpu
   auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  channel_out_ = SizeToInt(input_shape[0]);
-  min_size_ = sizeof(float) * channel_out_;
-  max_size_ = sizeof(float) * channel_out_;
+  num_channels_ = SizeToInt(input_shape[0]);
   input_size_ = sizeof(float);
   for (size_t i = 0; i < input_shape.size(); i++) {
     input_size_ *= input_shape[i];
   }
-  output_size_ = input_size_;
-
   InitSizeLists();
   return true;
 }
 
 void FakeQuantPerChannelGpuKernel::InitSizeLists() {
-  input_size_list_.push_back(input_size_);                       // input in tensor
-  input_size_list_.push_back(min_size_);                         // min one scalar
-  input_size_list_.push_back(max_size_);                         // max on scalar
-  output_size_list_.push_back(output_size_);                     // output in tensor
-  workspace_size_list_.push_back(sizeof(float) * channel_out_);  // scale in channel
-  workspace_size_list_.push_back(sizeof(float) * channel_out_);  // min in channel
-  workspace_size_list_.push_back(sizeof(float) * channel_out_);  // max in channel
+  input_size_list_.push_back(input_size_);                        // input in tensor
+  input_size_list_.push_back(sizeof(float) * num_channels_);      // min one scalar
+  input_size_list_.push_back(sizeof(float) * num_channels_);      // max on scalar
+  output_size_list_.push_back(input_size_);                       // output in tensor
+  workspace_size_list_.push_back(sizeof(float) * num_channels_);  // scale in channel
+  workspace_size_list_.push_back(sizeof(float) * num_channels_);  // min in channel
+  workspace_size_list_.push_back(sizeof(float) * num_channels_);  // max in channel
 }
 
-void FakeQuantPerChannelGpuKernel::CalFakeQuantizeForTraining(float *input, float *output, float *input_min,
-                                                              float *input_max, float *d_nudge_min, float *d_nudge_max,
-                                                              float *d_scale, void *stream_ptr) {
-  // calculate the input min and max according by the parameter ema and ema_decay.
-  CalMinMaxPerChannel(input, input_min, input_max, input_size_ / sizeof(float), channel_out_, ema_decay_, ema_,
-                      reinterpret_cast<cudaStream_t>(stream_ptr));
-  // control flow for quant_delay
-  if (global_step_ >= quant_delay_) {
-    // real launch
-    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
-                       reinterpret_cast<cudaStream_t>(stream_ptr));
-    CalFakeQuantizePerChannel(input, output, input_size_ / sizeof(float), channel_out_, d_nudge_min, d_nudge_max,
-                              d_scale, symmetric_, reinterpret_cast<cudaStream_t>(stream_ptr));
-  } else {
-    CHECK_CUDA_RET_WITH_ERROR(
-      cudaMemcpyAsync(output, input, input_size_, cudaMemcpyDeviceToDevice, reinterpret_cast<cudaStream_t>(stream_ptr)),
-      "Copy gpu memory failed.");
-  }
-  global_step_++;
-}
-
-void FakeQuantPerChannelGpuKernel::CalFakeQuantizeForInfer(float *input, float *output, float *input_min,
-                                                           float *input_max, float *d_nudge_min, float *d_nudge_max,
-                                                           float *d_scale, void *stream_ptr) {
-  // real launch
-  CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
-                     reinterpret_cast<cudaStream_t>(stream_ptr));
-  CalFakeQuantizePerChannel(input, output, input_size_ / sizeof(float), channel_out_, d_nudge_min, d_nudge_max, d_scale,
-                            symmetric_, reinterpret_cast<cudaStream_t>(stream_ptr));
+void FakeQuantPerChannelGpuKernel::CalFakeQuantize(float *input, float *output, float *input_min, float *input_max,
+                                                   float *nudge_min, float *nudge_max, float *scale, void *stream_ptr) {
+  CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, nudge_min, nudge_max, scale, num_channels_,
+                     symmetric_, reinterpret_cast<cudaStream_t>(stream_ptr));
+  CalFakeQuantPerChannel(input, output, input_size_ / sizeof(float), num_channels_, nudge_min, nudge_max, scale,
+                         reinterpret_cast<cudaStream_t>(stream_ptr));
 }
 
 bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
@@ -155,9 +115,9 @@ bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
   float *input = GetDeviceAddress<float>(inputs, 0);
   float *input_min = GetDeviceAddress<float>(inputs, 1);
   float *input_max = GetDeviceAddress<float>(inputs, 2);
-  float *d_scale = GetDeviceAddress<float>(workspace, 0);
-  float *d_nudge_min = GetDeviceAddress<float>(workspace, 1);
-  float *d_nudge_max = GetDeviceAddress<float>(workspace, 2);
+  float *scale = GetDeviceAddress<float>(workspace, 0);
+  float *nudge_min = GetDeviceAddress<float>(workspace, 1);
+  float *nudge_max = GetDeviceAddress<float>(workspace, 2);
 
   if (input == nullptr) {
     MS_LOG(EXCEPTION) << "FakeQuantPerChannelGpuKernel input is null.";
@@ -167,9 +127,16 @@ bool FakeQuantPerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
   }
 
   if (training_) {
-    CalFakeQuantizeForTraining(input, output, input_min, input_max, d_nudge_min, d_nudge_max, d_scale, stream_ptr);
+    if (global_step_ >= quant_delay_) {
+      CalFakeQuantize(input, output, input_min, input_max, nudge_min, nudge_max, scale, stream_ptr);
+    } else {
+      CHECK_CUDA_RET_WITH_ERROR(cudaMemcpyAsync(output, input, input_size_, cudaMemcpyDeviceToDevice,
+                                                reinterpret_cast<cudaStream_t>(stream_ptr)),
+                                "Copy gpu memory failed.");
+    }
+    global_step_++;
   } else {
-    CalFakeQuantizeForInfer(input, output, input_min, input_max, d_nudge_min, d_nudge_max, d_scale, stream_ptr);
+    CalFakeQuantize(input, output, input_min, input_max, nudge_min, nudge_max, scale, stream_ptr);
   }
 
   return true;

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perchannel_gpu_kernel.h

@@ -39,31 +39,23 @@ class FakeQuantPerChannelGpuKernel : public GpuKernel {
   void InitSizeLists() override;
 
  private:
-  void CalFakeQuantizeForTraining(float *input, float *output, float *input_min, float *input_max, float *d_nudge_min,
-                                  float *d_nudge_max, float *d_scale, void *stream_ptr);
-  void CalFakeQuantizeForInfer(float *input, float *output, float *input_min, float *input_max, float *d_nudge_min,
-                               float *d_nudge_max, float *d_scale, void *stream_ptr);
+  void CalFakeQuantize(float *input, float *output, float *input_min, float *input_max, float *nudge_min,
+                       float *nudge_max, float *scale, void *stream_ptr);
 
   size_t input_size_;
-  size_t min_size_;
-  size_t max_size_;
-  size_t output_size_;
-  size_t workspace_size_;
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;
   std::vector<size_t> workspace_size_list_;
 
+  int num_channels_;
   int num_bits_;
+  bool training_;
+  bool symmetric_;
+  bool narrow_range_;
+  int quant_delay_;
   float quant_min_;
   float quant_max_;
-  int quant_delay_;
-  bool ema_;
-  float ema_decay_;
   int global_step_;
-  bool training_;
-  int channel_out_;
-  bool narrow_range_;
-  bool symmetric_;
 };
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perchannel_grad_gpu_kernel.cc

@@ -14,21 +14,17 @@
  * limitations under the License.
  */
 
-#include "kernel/gpu/quant/fake_quant_per_channel_grad_gpu_kernel.h"
-#include "kernel/gpu/cuda_impl/fake_quant_per_channel_impl.cuh"
+#include "kernel/gpu/quant/fake_quant_perchannel_grad_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_perchannel_impl.cuh"
 
 namespace mindspore {
 namespace kernel {
 FakeQuantPerChannelGradGpuKernel::FakeQuantPerChannelGradGpuKernel()
     : input_size_(0),
-      min_size_(0),
-      max_size_(0),
-      output_size_(0),
-      workspace_size_(0),
       num_bits_(0),
       quant_min_(0),
       quant_max_(0),
-      channel_out_(0),
+      num_channels_(0),
       quant_delay_(0),
       global_step_(0),
       narrow_range_(false),
@@ -64,42 +60,34 @@ bool FakeQuantPerChannelGradGpuKernel::Init(const CNodePtr &kernel_node) {
   }
 
   symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
-  if (symmetric_) {
-    quant_min_ = 0 - (1 << (num_bits_ - 1));
-    quant_max_ = (1 << (num_bits_ - 1)) - 1;
-  } else {
-    quant_min_ = 0;
-    quant_max_ = (1 << num_bits_) - 1;
-  }
-
   narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+
+  // quant min and max value
+  quant_min_ = 0;
+  quant_max_ = (1 << num_bits_) - 1;
   if (narrow_range_) {
     quant_min_++;
   }
 
   auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-  channel_out_ = SizeToInt(input_shape[0]);
-  min_size_ = sizeof(float) * channel_out_;
-  max_size_ = sizeof(float) * channel_out_;
+  num_channels_ = SizeToInt(input_shape[0]);
   input_size_ = sizeof(float);
   for (size_t i = 0; i < input_shape.size(); i++) {
     input_size_ *= input_shape[i];
   }
-  output_size_ = input_size_;
-
   InitSizeLists();
   return true;
 }
 
 void FakeQuantPerChannelGradGpuKernel::InitSizeLists() {
-  input_size_list_.push_back(input_size_);  // gradient
-  input_size_list_.push_back(input_size_);  // input
-  input_size_list_.push_back(min_size_);    // min
-  input_size_list_.push_back(max_size_);    // max
-  output_size_list_.push_back(output_size_);
-  workspace_size_list_.push_back(sizeof(float) * channel_out_);  // scale in channel
-  workspace_size_list_.push_back(sizeof(float) * channel_out_);  // min in channel
-  workspace_size_list_.push_back(sizeof(float) * channel_out_);  // max in channel
+  input_size_list_.push_back(input_size_);                        // gradient
+  input_size_list_.push_back(input_size_);                        // input
+  input_size_list_.push_back(sizeof(float) * num_channels_);      // min
+  input_size_list_.push_back(sizeof(float) * num_channels_);      // max
+  output_size_list_.push_back(input_size_);                       // output
+  workspace_size_list_.push_back(sizeof(float) * num_channels_);  // scale in channel
+  workspace_size_list_.push_back(sizeof(float) * num_channels_);  // min in channel
+  workspace_size_list_.push_back(sizeof(float) * num_channels_);  // max in channel
 }
 
 bool FakeQuantPerChannelGradGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
@@ -111,9 +99,9 @@ bool FakeQuantPerChannelGradGpuKernel::Launch(const std::vector<AddressPtr> &inp
   float *input = GetDeviceAddress<float>(inputs, 1);
   float *input_min = GetDeviceAddress<float>(inputs, 2);
   float *input_max = GetDeviceAddress<float>(inputs, 3);
-  float *d_scale = GetDeviceAddress<float>(workspace, 0);
-  float *d_nudge_min = GetDeviceAddress<float>(workspace, 1);
-  float *d_nudge_max = GetDeviceAddress<float>(workspace, 2);
+  float *scale = GetDeviceAddress<float>(workspace, 0);
+  float *nudge_min = GetDeviceAddress<float>(workspace, 1);
+  float *nudge_max = GetDeviceAddress<float>(workspace, 2);
 
   if (gradient == nullptr) {
     MS_LOG(EXCEPTION) << "FakeQuantPerChannelGradGpuKernel gradient is null";
@@ -130,10 +118,10 @@ bool FakeQuantPerChannelGradGpuKernel::Launch(const std::vector<AddressPtr> &inp
 
   int total_size = input_size_ / sizeof(float);
   if (global_step_ >= quant_delay_) {
-    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, d_nudge_min, d_nudge_max, d_scale, channel_out_,
-                       reinterpret_cast<cudaStream_t>(stream_ptr));
-    CalFakeQuantizePerChannelGrad(input, gradient, output, total_size, channel_out_, d_nudge_min, d_nudge_max,
-                                  reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalNudgePerChannel(input_min, input_max, quant_min_, quant_max_, nudge_min, nudge_max, scale, num_channels_,
+                       symmetric_, reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantPerChannelGrad(input, gradient, output, total_size, num_channels_, nudge_min, nudge_max,
+                               reinterpret_cast<cudaStream_t>(stream_ptr));
   } else {
     CHECK_CUDA_RET_WITH_ERROR(cudaMemcpyAsync(output, gradient, input_size_, cudaMemcpyDeviceToDevice,
                                               reinterpret_cast<cudaStream_t>(stream_ptr)),

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perchannel_grad_gpu_kernel.h

@@ -40,10 +40,6 @@ class FakeQuantPerChannelGradGpuKernel : public GpuKernel {
 
  private:
   size_t input_size_;
-  size_t min_size_;
-  size_t max_size_;
-  size_t output_size_;
-  size_t workspace_size_;
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;
   std::vector<size_t> workspace_size_list_;
@@ -51,7 +47,7 @@ class FakeQuantPerChannelGradGpuKernel : public GpuKernel {
   int num_bits_;
   float quant_min_;
   float quant_max_;
-  int channel_out_;
+  int num_channels_;
   int quant_delay_;
   int global_step_;
   bool narrow_range_;

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perlayer_gpu_kernel.cc

@@ -0,0 +1,143 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include "kernel/gpu/quant/fake_quant_perlayer_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_perlayer_impl.cuh"
+#include <thrust/extrema.h>
+#include <thrust/pair.h>
+#include <thrust/device_vector.h>
+#include <cuda_runtime_api.h>
+
+namespace mindspore {
+namespace kernel {
+FakeQuantPerLayerGpuKernel::FakeQuantPerLayerGpuKernel()
+    : input_size_(0),
+      quant_min_(0),
+      quant_max_(0),
+      quant_num_(1),
+      global_step_(0),
+      num_bits_(0),
+      quant_delay_(0),
+      training_(false),
+      narrow_range_(false),
+      symmetric_(false) {}
+
+const std::vector<size_t> &FakeQuantPerLayerGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerLayerGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerLayerGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool FakeQuantPerLayerGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 3) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuant GpuKernel OP needs 3 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuant GpuKernel OP needs 1 output.";
+  }
+
+  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
+  training_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("training"));
+  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
+  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+
+  if (num_bits_ <= 2 || num_bits_ >= 16) {
+    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
+  }
+
+  if (quant_delay_ < 0) {
+    MS_LOG(EXCEPTION) << "Attr \'quant_delay\' " << num_bits_ << "is less then 0, require larger than 0.";
+  }
+
+  // quant min and max value
+  quant_min_ = 0;
+  quant_max_ = (1 << num_bits_) - 1;
+  if (narrow_range_) {
+    quant_min_++;
+  }
+
+  // init size
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); ++i) {
+    quant_num_ *= SizeToInt(input_shape[i]);
+  }
+  input_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  InitSizeLists();
+  return true;
+}
+
+void FakeQuantPerLayerGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);        // x
+  input_size_list_.push_back(sizeof(float));      // min
+  input_size_list_.push_back(sizeof(float));      // max
+  output_size_list_.push_back(input_size_);       // y
+  workspace_size_list_.push_back(sizeof(float));  // scale
+  workspace_size_list_.push_back(sizeof(float));  // nudge_min
+  workspace_size_list_.push_back(sizeof(float));  // nudge_max
+}
+
+bool FakeQuantPerLayerGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+                                        const std::vector<AddressPtr> &outputs, void *stream_ptr) {
+  float *output = GetDeviceAddress<float>(outputs, 0);
+  float *input = GetDeviceAddress<float>(inputs, 0);
+  float *input_min = GetDeviceAddress<float>(inputs, 1);
+  float *input_max = GetDeviceAddress<float>(inputs, 2);
+  float *scale = GetDeviceAddress<float>(workspace, 0);
+  float *nudge_min = GetDeviceAddress<float>(workspace, 1);
+  float *nudge_max = GetDeviceAddress<float>(workspace, 2);
+
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerLayerGpuKernel input x is null.";
+  }
+  if (input_min == nullptr || input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerLayerGpuKernel input min or input max is null.";
+  }
+
+  if (training_) {
+    // control flow for quant_delay
+    if (global_step_ >= quant_delay_) {
+      // real launch
+      CalNudgePerLayer(input_min, input_max, quant_min_, quant_max_, nudge_min, nudge_max, scale, symmetric_,
+                       reinterpret_cast<cudaStream_t>(stream_ptr));
+      CalFakeQuantPerLayer(input, output, quant_num_, nudge_min, nudge_max, scale,
+                           reinterpret_cast<cudaStream_t>(stream_ptr));
+    } else {
+      CHECK_CUDA_RET_WITH_ERROR(cudaMemcpyAsync(output, input, input_size_, cudaMemcpyDeviceToDevice,
+                                                reinterpret_cast<cudaStream_t>(stream_ptr)),
+                                "Copy gpu memory failed");
+    }
+    global_step_++;
+  } else {
+    // real launch
+    CalNudgePerLayer(input_min, input_max, quant_min_, quant_max_, nudge_min, nudge_max, scale, symmetric_,
+                     reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantPerLayer(input, output, quant_num_, nudge_min, nudge_max, scale,
+                         reinterpret_cast<cudaStream_t>(stream_ptr));
+  }
+
+  return true;
+}
+
+MS_REG_GPU_KERNEL(FakeQuantPerLayer, FakeQuantPerLayerGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perlayer_gpu_kernel.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GPUKERNEL_H_
-#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GPUKERNEL_H_
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PERLAYER_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PERLAYER_GPUKERNEL_H_
 
 #include <vector>
 #include "kernel/gpu/gpu_kernel.h"
@@ -23,10 +23,10 @@
 
 namespace mindspore {
 namespace kernel {
-class FakeQuantGpuKernel : public GpuKernel {
+class FakeQuantPerLayerGpuKernel : public GpuKernel {
  public:
-  FakeQuantGpuKernel();
-  ~FakeQuantGpuKernel() = default;
+  FakeQuantPerLayerGpuKernel();
+  ~FakeQuantPerLayerGpuKernel() = default;
 
   const std::vector<size_t> &GetInputSizeList() const override;
   const std::vector<size_t> &GetOutputSizeList() const override;
@@ -40,22 +40,16 @@ class FakeQuantGpuKernel : public GpuKernel {
 
  private:
   size_t input_size_;
-  size_t min_size_;
-  size_t max_size_;
-  size_t output_size_;
-  size_t workspace_size_;
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;
   std::vector<size_t> workspace_size_list_;
 
-  int num_bits_;
   float quant_min_;
   float quant_max_;
   int quant_num_;
-  int quant_delay_;
-  bool ema_;
-  float ema_decay_;
   int global_step_;
+  int num_bits_;
+  int quant_delay_;
   bool training_;
   bool narrow_range_;
   bool symmetric_;
@@ -63,4 +57,4 @@ class FakeQuantGpuKernel : public GpuKernel {
 }  // namespace kernel
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GPUKERNEL_H_
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PERLAYER_GPUKERNEL_H_

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perlayer_grad_gpu_kernel.cc

@@ -0,0 +1,133 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include "kernel/gpu/quant/fake_quant_perlayer_grad_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/fake_quant_perlayer_impl.cuh"
+
+namespace mindspore {
+namespace kernel {
+FakeQuantPerLayerGradGpuKernel::FakeQuantPerLayerGradGpuKernel()
+    : input_size_(0),
+      workspace_size_(0),
+      num_bits_(0),
+      quant_min_(0),
+      quant_max_(0),
+      quant_num_(1),
+      quant_delay_(0),
+      global_step_(0),
+      narrow_range_(false),
+      symmetric_(false) {}
+
+const std::vector<size_t> &FakeQuantPerLayerGradGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerLayerGradGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &FakeQuantPerLayerGradGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool FakeQuantPerLayerGradGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 4) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuantGrad GpuKernel OP needs 4 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 1) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuantGrad GpuKernel OP needs 1 output.";
+  }
+
+  num_bits_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("num_bits"));
+  if (num_bits_ <= 2 || num_bits_ >= 16) {
+    MS_LOG(EXCEPTION) << "Attr \'num_bits\' " << num_bits_ << " is out of range, expected between 2 and 16.";
+  }
+
+  quant_delay_ = GetValue<int>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("quant_delay"));
+  if (quant_delay_ < 0) {
+    MS_LOG(EXCEPTION) << "Attr \'quant_delay_\' " << quant_delay_ << " is less then 0, require larger than 0.";
+  }
+
+  symmetric_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("symmetric"));
+  narrow_range_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("narrow_range"));
+
+  // quant min and max value
+  quant_min_ = 0;
+  quant_max_ = (1 << num_bits_) - 1;
+  if (narrow_range_) {
+    quant_min_++;
+  }
+
+  // init size
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); ++i) {
+    quant_num_ *= SizeToInt(input_shape[i]);
+  }
+  input_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  InitSizeLists();
+  return true;
+}
+
+void FakeQuantPerLayerGradGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);        // gradient
+  input_size_list_.push_back(input_size_);        // input
+  input_size_list_.push_back(sizeof(float));      // min
+  input_size_list_.push_back(sizeof(float));      // max
+  output_size_list_.push_back(input_size_);       // output
+  workspace_size_list_.push_back(sizeof(float));  // scale
+  workspace_size_list_.push_back(sizeof(float));  // nudge_min
+  workspace_size_list_.push_back(sizeof(float));  // nudge_max
+}
+
+bool FakeQuantPerLayerGradGpuKernel::Launch(const std::vector<AddressPtr> &inputs,
+                                            const std::vector<AddressPtr> &workspace,
+                                            const std::vector<AddressPtr> &outputs, void *stream_ptr) {
+  float *output = GetDeviceAddress<float>(outputs, 0);
+  float *gradient = GetDeviceAddress<float>(inputs, 0);
+  float *input = GetDeviceAddress<float>(inputs, 1);
+  float *input_min = GetDeviceAddress<float>(inputs, 2);
+  float *input_max = GetDeviceAddress<float>(inputs, 3);
+  float *scale = GetDeviceAddress<float>(workspace, 0);
+  float *nudge_min = GetDeviceAddress<float>(workspace, 1);
+  float *nudge_max = GetDeviceAddress<float>(workspace, 2);
+
+  if (gradient == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerLayerGradGpuKernel gradient is null";
+  }
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerLayerGradGpuKernel input is null.";
+  }
+  if (input_min == nullptr || input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "FakeQuantPerLayerGradGpuKernel input min or max is null.";
+  }
+
+  if (global_step_ >= quant_delay_) {
+    CalNudgePerLayer(input_min, input_max, quant_min_, quant_max_, nudge_min, nudge_max, scale, symmetric_,
+                     reinterpret_cast<cudaStream_t>(stream_ptr));
+    CalFakeQuantPerLayerGrad(input, gradient, output, quant_num_, nudge_min, nudge_max,
+                             reinterpret_cast<cudaStream_t>(stream_ptr));
+  } else {
+    CHECK_CUDA_RET_WITH_ERROR(cudaMemcpyAsync(output, gradient, input_size_, cudaMemcpyDeviceToDevice,
+                                              reinterpret_cast<cudaStream_t>(stream_ptr)),
+                              "Copy gpu memory failed");
+  }
+  global_step_++;
+  return true;
+}
+
+MS_REG_GPU_KERNEL(FakeQuantPerLayerGrad, FakeQuantPerLayerGradGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/fake_quant_perlayer_grad_gpu_kernel.h

@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GRAD_GPUKERNEL_H_
-#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GRAD_GPUKERNEL_H_
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PERLAYER_GRAD_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PERLAYER_GRAD_GPUKERNEL_H_
 
 #include <vector>
 #include "kernel/gpu/gpu_kernel.h"
@@ -23,10 +23,10 @@
 
 namespace mindspore {
 namespace kernel {
-class FakeQuantGradGpuKernel : public GpuKernel {
+class FakeQuantPerLayerGradGpuKernel : public GpuKernel {
  public:
-  FakeQuantGradGpuKernel();
-  ~FakeQuantGradGpuKernel() = default;
+  FakeQuantPerLayerGradGpuKernel();
+  ~FakeQuantPerLayerGradGpuKernel() = default;
 
   const std::vector<size_t> &GetInputSizeList() const override;
   const std::vector<size_t> &GetOutputSizeList() const override;
@@ -40,9 +40,6 @@ class FakeQuantGradGpuKernel : public GpuKernel {
 
  private:
   size_t input_size_;
-  size_t min_size_;
-  size_t max_size_;
-  size_t output_size_;
   size_t workspace_size_;
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;
@@ -51,7 +48,7 @@ class FakeQuantGradGpuKernel : public GpuKernel {
   int num_bits_;
   float quant_min_;
   float quant_max_;
-  int quant_size_;
+  int quant_num_;
   int quant_delay_;
   int global_step_;
   bool narrow_range_;
@@ -60,4 +57,4 @@ class FakeQuantGradGpuKernel : public GpuKernel {
 }  // namespace kernel
 }  // namespace mindspore
 
-#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_GRAD_GPUKERNEL_H_
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_FAKEQUANT_PERLAYER_GRAD_GPUKERNEL_H_

mindspore/ccsrc/kernel/gpu/quant/minmax_update_perchannel_gpu_kernel.cc

@@ -0,0 +1,96 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include "kernel/gpu/quant/minmax_update_perchannel_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/minmax_update_impl.cuh"
+#include <thrust/extrema.h>
+#include <thrust/pair.h>
+#include <thrust/device_vector.h>
+#include <cuda_runtime_api.h>
+
+namespace mindspore {
+namespace kernel {
+MinMaxUpdatePerChannelGpuKernel::MinMaxUpdatePerChannelGpuKernel()
+    : input_size_(0), quant_num_(1), ema_(false), ema_decay_(0), num_channels_(0) {}
+
+const std::vector<size_t> &MinMaxUpdatePerChannelGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &MinMaxUpdatePerChannelGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &MinMaxUpdatePerChannelGpuKernel::GetWorkspaceSizeList() const {
+  return workspace_size_list_;
+}
+
+bool MinMaxUpdatePerChannelGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 3) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuant GpuKernel OP needs 3 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 2) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuant GpuKernel OP needs 1 output.";
+  }
+
+  ema_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema"));
+  ema_decay_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema_decay"));
+
+  // init size
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  num_channels_ = SizeToInt(input_shape[0]);
+  for (size_t i = 0; i < input_shape.size(); ++i) {
+    quant_num_ *= SizeToInt(input_shape[i]);
+  }
+  input_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  InitSizeLists();
+  return true;
+}
+
+void MinMaxUpdatePerChannelGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);                     // input
+  input_size_list_.push_back(sizeof(float) * num_channels_);   // min
+  input_size_list_.push_back(sizeof(float) * num_channels_);   // max
+  output_size_list_.push_back(sizeof(float) * num_channels_);  // output min
+  output_size_list_.push_back(sizeof(float) * num_channels_);  // output max
+}
+
+bool MinMaxUpdatePerChannelGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+                                             const std::vector<AddressPtr> &outputs, void *stream_ptr) {
+  float *output_min = GetDeviceAddress<float>(outputs, 0);
+  float *output_max = GetDeviceAddress<float>(outputs, 1);
+  float *input = GetDeviceAddress<float>(inputs, 0);
+  float *input_min = GetDeviceAddress<float>(inputs, 1);
+  float *input_max = GetDeviceAddress<float>(inputs, 2);
+
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "MinMaxUpdatePerChannelGpuKernel input x is null.";
+  }
+  if (input_min == nullptr || input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "MinMaxUpdatePerChannelGpuKernel input min or input max is null.";
+  }
+
+  // calculate the input min and max according by the parameter ema and ema_decay.
+  CalMinMaxPerChannel(input, input_min, input_max, output_min, output_max, input_size_ / sizeof(float), num_channels_,
+                      ema_decay_, ema_, reinterpret_cast<cudaStream_t>(stream_ptr));
+  return true;
+}
+
+MS_REG_GPU_KERNEL(MinMaxUpdatePerChannel, MinMaxUpdatePerChannelGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/minmax_update_perchannel_gpu_kernel.h

@@ -0,0 +1,55 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_MINMAX_UPDATE_PERCHANNEL_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_MINMAX_UPDATE_PERCHANNEL_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class MinMaxUpdatePerChannelGpuKernel : public GpuKernel {
+ public:
+  MinMaxUpdatePerChannelGpuKernel();
+  ~MinMaxUpdatePerChannelGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override;
+  bool Init(const CNodePtr &kernel) override;
+
+ protected:
+  void InitSizeLists() override;
+
+ private:
+  size_t input_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  int quant_num_;
+  bool ema_;
+  float ema_decay_;
+  int num_channels_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_MINMAX_UPDATE_PERCHANNEL_GPUKERNEL_H_

mindspore/ccsrc/kernel/gpu/quant/minmax_update_perlayer_gpu_kernel.cc

@@ -0,0 +1,93 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#include "kernel/gpu/quant/minmax_update_perlayer_gpu_kernel.h"
+#include "kernel/gpu/cuda_impl/minmax_update_impl.cuh"
+#include <thrust/extrema.h>
+#include <thrust/pair.h>
+#include <thrust/device_vector.h>
+#include <cuda_runtime_api.h>
+
+namespace mindspore {
+namespace kernel {
+MinMaxUpdatePerLayerGpuKernel::MinMaxUpdatePerLayerGpuKernel()
+    : input_size_(0), quant_num_(1), ema_(false), ema_decay_(0) {}
+
+const std::vector<size_t> &MinMaxUpdatePerLayerGpuKernel::GetInputSizeList() const { return input_size_list_; }
+
+const std::vector<size_t> &MinMaxUpdatePerLayerGpuKernel::GetOutputSizeList() const { return output_size_list_; }
+
+const std::vector<size_t> &MinMaxUpdatePerLayerGpuKernel::GetWorkspaceSizeList() const { return workspace_size_list_; }
+
+bool MinMaxUpdatePerLayerGpuKernel::Init(const CNodePtr &kernel_node) {
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num != 3) {
+    MS_LOG(EXCEPTION) << "Input number is " << input_num << ", but FakeQuant GpuKernel OP needs 3 output.";
+  }
+
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num != 2) {
+    MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but FakeQuant GpuKernel OP needs 1 output.";
+  }
+
+  ema_ = GetValue<bool>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema"));
+  ema_decay_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("ema_decay"));
+
+  // init size
+  auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  for (size_t i = 0; i < input_shape.size(); ++i) {
+    quant_num_ *= SizeToInt(input_shape[i]);
+  }
+  input_size_ = sizeof(float);
+  for (size_t i = 0; i < input_shape.size(); i++) {
+    input_size_ *= input_shape[i];
+  }
+  InitSizeLists();
+  return true;
+}
+
+void MinMaxUpdatePerLayerGpuKernel::InitSizeLists() {
+  input_size_list_.push_back(input_size_);     // input
+  input_size_list_.push_back(sizeof(float));   // input min
+  input_size_list_.push_back(sizeof(float));   // input max
+  output_size_list_.push_back(sizeof(float));  // output min
+  output_size_list_.push_back(sizeof(float));  // output max
+}
+
+bool MinMaxUpdatePerLayerGpuKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
+                                           const std::vector<AddressPtr> &outputs, void *stream_ptr) {
+  float *output_min = GetDeviceAddress<float>(outputs, 0);
+  float *output_max = GetDeviceAddress<float>(outputs, 1);
+  float *input = GetDeviceAddress<float>(inputs, 0);
+  float *input_min = GetDeviceAddress<float>(inputs, 1);
+  float *input_max = GetDeviceAddress<float>(inputs, 2);
+
+  if (input == nullptr) {
+    MS_LOG(EXCEPTION) << "MinMaxUpdatePerLayerGpuKernel input x is null.";
+  }
+  if (input_min == nullptr || input_max == nullptr) {
+    MS_LOG(EXCEPTION) << "MinMaxUpdatePerLayerGpuKernel input min or input max is null.";
+  }
+
+  CalMinMaxPerLayer(input, input_min, input_max, output_min, output_max, quant_num_, ema_decay_, ema_,
+                    reinterpret_cast<cudaStream_t>(stream_ptr));
+
+  return true;
+}
+
+MS_REG_GPU_KERNEL(MinMaxUpdatePerLayer, MinMaxUpdatePerLayerGpuKernel)
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/kernel/gpu/quant/minmax_update_perlayer_gpu_kernel.h

@@ -0,0 +1,54 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_KERNEL_GPU_MINMAX_UPDATE_PERLAYER_GPUKERNEL_H_
+#define MINDSPORE_CCSRC_KERNEL_GPU_MINMAX_UPDATE_PERLAYER_GPUKERNEL_H_
+
+#include <vector>
+#include "kernel/gpu/gpu_kernel.h"
+#include "kernel/gpu/gpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class MinMaxUpdatePerLayerGpuKernel : public GpuKernel {
+ public:
+  MinMaxUpdatePerLayerGpuKernel();
+  ~MinMaxUpdatePerLayerGpuKernel() = default;
+
+  const std::vector<size_t> &GetInputSizeList() const override;
+  const std::vector<size_t> &GetOutputSizeList() const override;
+  const std::vector<size_t> &GetWorkspaceSizeList() const override;
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs, void *stream_ptr) override;
+  bool Init(const CNodePtr &kernel) override;
+
+ protected:
+  void InitSizeLists() override;
+
+ private:
+  size_t input_size_;
+  std::vector<size_t> input_size_list_;
+  std::vector<size_t> output_size_list_;
+  std::vector<size_t> workspace_size_list_;
+
+  int quant_num_;
+  bool ema_;
+  float ema_decay_;
+};
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_KERNEL_GPU_MINMAX_UPDATE_PERLAYER_GPUKERNEL_H_

mindspore/ccsrc/kernel/kernel_query.cc

@@ -23,6 +23,7 @@
 #include "kernel/tbe/tbe_kernel_select/tbe_kernel_select.h"
 #include "kernel/akg/akg_kernel_metadata.h"
 #include "session/anf_runtime_algorithm.h"
+#include "utils/context/ms_context.h"
 
 namespace mindspore {
 namespace kernel {
@@ -96,6 +97,12 @@ void KernelQuery(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel
 
   std::string op_name = AnfAlgo::GetCNodeName(kernel_node);
 
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  if (context_ptr->enable_graph_kernel() && IsPrimitiveCNode(kernel_node, prim::kPrimBatchMatMul)) {
+    kernel_type = KernelType::AKG_KERNEL;
+  }
+
   switch (kernel_type) {
     case KernelType::AKG_KERNEL:
       AkgMetadataInfo(kernel_node, kernel_info_list);

mindspore/ccsrc/kernel/rts/label_switch.cc

@@ -75,8 +75,8 @@ std::vector<TaskInfoPtr> LabelSwitchKernel::GenTask(const std::vector<AddressPtr
 
 std::vector<std::shared_ptr<kernel::KernelBuildInfo>> LabelSwitchDesc::GetKernelInfo() {
   std::vector<std::shared_ptr<kernel::KernelBuildInfo>> label_switch_build_info{};
-  vector<string> input_format{kOpFormat_DEFAULT, kOpFormat_DEFAULT};
-  vector<TypeId> input_type{kNumberTypeUInt32, kNumberTypeBool};
+  vector<string> input_format{kOpFormat_DEFAULT};
+  vector<TypeId> input_type{kNumberTypeInt32};
   if (input_format.size() != input_type.size()) {
     MS_LOG(EXCEPTION) << "Invalid param num, input_format size " << input_format.size() << " input_type size "
                       << input_type.size();

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.cc

@@ -28,7 +28,6 @@
 
 namespace mindspore {
 namespace parallel {
-#define DOUBLE_MAX (std::numeric_limits<double>::max)()
 
 // Compute redistributed cost
 double CostRedis(const Graph::NodeType &node,
@@ -621,75 +620,50 @@ StrategyRec CostCommon::ChoseStr(const std::vector<double> &cost_op, StrategyRec
       break;
 
     default:
-      MS_LOG(EXCEPTION) << "Failure: CostBiasAdd failed.";
+      MS_LOG(EXCEPTION) << "Failure: Common failed.";
   }
   return str;
 }
 
-// Get weight for BN
-double CostBatchNorm::GetMinCostIn(const OperatorRec &op) {
-  int tensor = static_cast<int>(op.arguments[0].tensor_shape.shape_h * op.arguments[0].tensor_str.str_h) *
-               static_cast<int>(op.arguments[0].tensor_shape.shape_n * op.arguments[0].tensor_str.str_n) *
-               static_cast<int>(op.arguments[0].tensor_shape.shape_w * op.arguments[0].tensor_str.str_w) *
-               static_cast<int>(op.arguments[0].tensor_shape.shape_c * op.arguments[0].tensor_str.str_c);
-
-  std::vector<double> cost_in;
-  cost_in.push_back(StrDimB(tensor) * 1.2);
-  cost_in.push_back(DOUBLE_MAX);
-  cost_in.push_back(StrDimH(tensor) * 1.2);
-  cost_in.push_back(StrDimW(tensor) * 1.2);
-
-  return *min_element(cost_in.begin(), cost_in.end());
-}
-
-// Get optimal strategy for BN
-StrategyRec CostBatchNorm::GetOptimalStr(const Graph::NodeType &node,
-                                         const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
-                                         const Graph &graph) {
+// Get optimal strategy for BatchParallel OPs
+StrategyRec CostBatchParallel::GetOptimalStr(const Graph::NodeType &node) {
   const OperatorRec &op = node.apply;
-
-  int tensor_filter_n = static_cast<int>(op.arguments[1].tensor_shape.shape_n * op.arguments[1].tensor_str.str_n);
-  int tensor_filter_c = static_cast<int>(op.arguments[1].tensor_shape.shape_c * op.arguments[1].tensor_str.str_c);
-  int tensor_filter_h = static_cast<int>(op.arguments[1].tensor_shape.shape_h * op.arguments[1].tensor_str.str_h);
-  int tensor_filter_w = static_cast<int>(op.arguments[1].tensor_shape.shape_w * op.arguments[1].tensor_str.str_w);
-
-  int tensor_filter = tensor_filter_h * tensor_filter_w * tensor_filter_n * tensor_filter_c;
-
-  int output_tensor_h = static_cast<int>(node.tensor_parm.tensor_shape.shape_h * node.tensor_parm.tensor_str.str_h);
-  int output_tensor_w = static_cast<int>(node.tensor_parm.tensor_shape.shape_w * node.tensor_parm.tensor_str.str_w);
-  int output_tensor_n = static_cast<int>(node.tensor_parm.tensor_shape.shape_n * node.tensor_parm.tensor_str.str_n);
+  int tensor_n = static_cast<int>(op.arguments[0].tensor_shape.shape_n * op.arguments[0].tensor_str.str_n);
+  int tensor_c = static_cast<int>(op.arguments[0].tensor_shape.shape_c * op.arguments[0].tensor_str.str_c);
+  int tensor_h = static_cast<int>(op.arguments[0].tensor_shape.shape_h * op.arguments[0].tensor_str.str_h);
+  int tensor_w = static_cast<int>(op.arguments[0].tensor_shape.shape_w * op.arguments[0].tensor_str.str_w);
 
   std::vector<double> cost_op;
-  std::vector<std::vector<float>> mode;
 
-  if (output_tensor_n < 2 || output_tensor_n % 2 != 0) {
+  if (tensor_n < 2 || tensor_n % 2 != 0) {
     cost_op.push_back(DOUBLE_MAX);
   } else {
-    cost_op.push_back(StrDimB(tensor_filter) + CostRedis(node, node_name_to_strategy,
-                                                         mode = {{0.5, 1, 1, 1}, {1, 1, 1, 1}, {0.5, 1, 1, 1}}, graph));
+    cost_op.push_back(cost_in_);
   }
 
-  cost_op.push_back(DOUBLE_MAX);
-
-  if (output_tensor_h < 2 || output_tensor_h % 2 != 0) {
+  if (tensor_c < 2 || tensor_c % 2 != 0) {
     cost_op.push_back(DOUBLE_MAX);
   } else {
-    cost_op.push_back(StrDimH(tensor_filter) + CostRedis(node, node_name_to_strategy,
-                                                         mode = {{1, 1, 0.5, 1}, {1, 1, 1, 1}, {1, 1, 0.5, 1}}, graph));
+    cost_op.push_back(cost_in_);
   }
 
-  if (output_tensor_w < 2 || output_tensor_w % 2 != 0) {
+  if (tensor_h < 2 || tensor_h % 2 != 0) {
     cost_op.push_back(DOUBLE_MAX);
   } else {
-    cost_op.push_back(StrDimW(tensor_filter) + CostRedis(node, node_name_to_strategy,
-                                                         mode = {{1, 1, 1, 0.5}, {1, 1, 1, 1}, {1, 1, 1, 0.5}}, graph));
+    cost_op.push_back(cost_in_);
+  }
+
+  if (tensor_w < 2 || tensor_w % 2 != 0) {
+    cost_op.push_back(DOUBLE_MAX);
+  } else {
+    cost_op.push_back(cost_in_);
   }
 
   return ChoseStr(cost_op, node.apply.str);
 }
 
-// Chose strategy for BatchNorm
-StrategyRec CostBatchNorm::ChoseStr(const std::vector<double> &cost_op, StrategyRec str) {
+// Chose strategy for BatchParallel op
+StrategyRec CostBatchParallel::ChoseStr(const std::vector<double> &cost_op, StrategyRec str) {
   uint64_t min_position = min_element(cost_op.begin(), cost_op.end()) - cost_op.begin();
   if (cost_op[min_position] > (DOUBLE_MAX - 0.1)) {
     return str;
@@ -700,36 +674,75 @@ StrategyRec CostBatchNorm::ChoseStr(const std::vector<double> &cost_op, Strategy
       str.inputTensor[0].str_n /= 2.0;
       str.outputTensor.str_n /= 2.0;
       str.cut_counter += 1;
-      str.cost = str.cost + cost_in_b_;
+      str.cost = str.cost + cost_in_;
       break;
 
     case 1:
       str.inputTensor[0].str_c /= 2.0;
-      str.inputTensor[1].str_c /= 2.0;
-      str.inputTensor[2].str_c /= 2.0;
-      str.inputTensor[3].str_c /= 2.0;
-      str.inputTensor[4].str_c /= 2.0;
       str.outputTensor.str_c /= 2.0;
       str.cut_counter += 1;
-      str.cost = str.cost + cost_in_c_;
+      str.cost = str.cost + cost_in_;
       break;
 
     case 2:
       str.inputTensor[0].str_h /= 2.0;
       str.outputTensor.str_h /= 2.0;
       str.cut_counter += 1;
-      str.cost = str.cost + cost_in_h_;
+      str.cost = str.cost + cost_in_;
       break;
 
     case 3:
       str.inputTensor[0].str_w /= 2.0;
       str.outputTensor.str_w /= 2.0;
       str.cut_counter += 1;
-      str.cost = str.cost + cost_in_w_;
+      str.cost = str.cost + cost_in_;
       break;
 
     default:
-      MS_LOG(EXCEPTION) << "Failure: CostBatchNorm failed.";
+      MS_LOG(EXCEPTION) << "Failure: CostBatchParallel failed.";
+  }
+  return str;
+}
+
+// Chose strategy for CostSoftmaxCrossEntropyWithLogits
+StrategyRec CostSoftmaxCrossEntropyWithLogits::ChoseStr(const std::vector<double> &cost_op, StrategyRec str) {
+  uint64_t min_position = min_element(cost_op.begin(), cost_op.end()) - cost_op.begin();
+  if (cost_op[min_position] > (DOUBLE_MAX - 0.1)) {
+    return str;
+  }
+
+  switch (min_position) {
+    case 0:
+      str.inputTensor[0].str_n /= 2.0;
+      str.inputTensor[1].str_n /= 2.0;
+      str.cut_counter += 1;
+      str.cost = str.cost + cost_in_;
+      break;
+
+    case 1:
+      str.inputTensor[0].str_c /= 2.0;
+      str.inputTensor[1].str_c /= 2.0;
+      str.cut_counter += 1;
+      str.cost = str.cost + cost_in_;
+      break;
+
+    case 2:
+      str.inputTensor[0].str_h /= 2.0;
+      str.inputTensor[1].str_h /= 2.0;
+      str.outputTensor.str_w /= 2.0;
+      str.cut_counter += 1;
+      str.cost = str.cost + cost_in_;
+      break;
+
+    case 3:
+      str.inputTensor[0].str_w /= 2.0;
+      str.inputTensor[1].str_w /= 2.0;
+      str.cut_counter += 1;
+      str.cost = str.cost + cost_in_;
+      break;
+
+    default:
+      MS_LOG(EXCEPTION) << "Failure: CostSoftmax failed.";
   }
   return str;
 }

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_cost.h

@@ -28,6 +28,8 @@
 
 namespace mindspore {
 namespace parallel {
+#define DOUBLE_MAX (std::numeric_limits<double>::max)()
+
 double CostRedis(const Graph::NodeType &node,
                  const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
                  const std::vector<std::vector<float>> &mode, const Graph &graph);
@@ -195,7 +197,6 @@ class CostTensorAdd : public CostCommon {
 };
 
 // all the following operation are element-wise and have the same cost
-class CostOneHot : public CostCommon {};
 class CostReLU : public CostCommon {};
 class CostLog : public CostCommon {};
 class CostExp : public CostCommon {};
@@ -206,50 +207,27 @@ class CostDiv : public CostCommon {};
 class CostSqueeze : public CostCommon {};
 class CostCast : public CostCommon {};
 
-// class BatchNorm is used to compute the cost of BatchNorm operator.
-class CostBatchNorm {
+// class BatchParallel is used to compute the cost of BatchParallel operator.
+class CostBatchParallel {
  public:
-  StrategyRec GetOptimalStr(const Graph::NodeType &node,
-                            const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
-                            const Graph &graph);
+  virtual StrategyRec GetOptimalStr(const Graph::NodeType &node);
 
-  double GetMinCostIn(const OperatorRec &op);
+  virtual double GetMaxCostIn() const { return DOUBLE_MAX; }
 
- private:
-  double StrDimB(int32_t Tensor) {
-    cost_in_b_ = (static_cast<double>(Tensor) * 4.0) / 2.0;
+ protected:
+  virtual StrategyRec ChoseStr(const std::vector<double> &cost_op, StrategyRec str);
 
-    return cost_in_b_;
-  }
+  double cost_in_ = 0;
+};  // class BatchParallel is used to compute the cost of BatchParallel operator.
 
-  double StrDimC() {
-    cost_in_c_ = 0.0;
-
-    return cost_in_c_;
-  }
-
-  double StrDimH(int32_t Tensor) {
-    cost_in_h_ = (static_cast<double>(Tensor) * 4.0) / 2.0;
-
-    return cost_in_h_;
-  }
-
-  double StrDimW(int32_t Tensor) {
-    cost_in_w_ = (static_cast<double>(Tensor) * 4.0) / 2.0;
-
-    return cost_in_w_;
-  }
+class CostBatchNorm : public CostBatchParallel {};
+class CostOneHot : public CostBatchParallel {};
+class CostPRelu : public CostBatchParallel {};
+class CostSoftmax : public CostBatchParallel {};
 
+class CostSoftmaxCrossEntropyWithLogits : public CostBatchParallel {
   StrategyRec ChoseStr(const std::vector<double> &cost_op, StrategyRec str);
-
-  double cost_in_b_ = 0;
-
-  double cost_in_c_ = 0;
-
-  double cost_in_h_ = 0;
-
-  double cost_in_w_ = 0;
-};  // class BatchNorm is used to compute the cost of BatchNorm operator.
+};
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // PARALLEL_AUTO_PARALLEL_REC_COST_H_

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.cc

@@ -28,10 +28,10 @@
 
 namespace mindspore {
 namespace parallel {
-void GenerateStrategy(std::shared_ptr<Graph> graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                      const std::shared_ptr<std::vector<std::vector<size_t>>> eli_list,
+void GenerateStrategy(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                      const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list,
                       const std::vector<std::vector<std::string>> &input_tensor_names,
-                      const std::shared_ptr<std::vector<size_t>> index_list) {
+                      const std::shared_ptr<std::vector<size_t>> &index_list) {
   MS_EXCEPTION_IF_NULL(graph);
   MS_EXCEPTION_IF_NULL(eli_list);
   MS_EXCEPTION_IF_NULL(index_list);
@@ -127,25 +127,6 @@ std::vector<std::vector<int32_t>> PrepareMatMul(const std::shared_ptr<Graph> &gr
   return strategies;
 }
 
-std::vector<std::vector<int32_t>> PreparePReLU(const std::shared_ptr<Graph> &graph,
-                                               const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                               const size_t iter_graph, const size_t iter_ops) {
-  std::vector<std::vector<int32_t>> strategies = MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
-  strategies[1][0] = 1;
-  return strategies;
-}
-
-std::vector<std::vector<int32_t>> PrepareBatchNorm(const std::shared_ptr<Graph> &graph,
-                                                   const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                                   const size_t iter_graph, const size_t iter_ops) {
-  std::vector<std::vector<int32_t>> strategies = MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
-  for (size_t i = 1; i < strategies.size(); i++) {
-    strategies[i][0] = strategies[0][1];
-  }
-  strategies[1][0] = 1;
-  return strategies;
-}
-
 std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vector<int32_t>> &s) {
   std::vector<std::vector<int32_t>> strategies;
   strategies.push_back(*s);
@@ -155,18 +136,88 @@ std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vect
   return strategies;
 }
 
-std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<std::vector<int32_t>> &s) {
-  std::vector<std::vector<int32_t>> strategies;
+std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<Graph> &graph,
+                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                const size_t iter_graph, const size_t iter_ops) {
+  std::vector<std::vector<int32_t>> strategies = MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);
+
+  int32_t axis = -1;
+  auto iter = ops[iter_ops]->attrs().find(AXIS);
+  if (iter != ops[iter_ops]->attrs().end()) {
+    MS_EXCEPTION_IF_NULL(iter->second);
+    if (iter->second->isa<Int32Imm>()) {
+      axis = iter->second->cast<Int32ImmPtr>()->value();
+    } else {
+      MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": The value of axis is not int.";
+    }
+  }
+  if (axis == -1) {
+    strategies[0][0] = strategies[0][1];
+    strategies[0][1] = 1;
+    graph->nodes[iter_graph].tensor_parm.tensor_str.str_h = graph->nodes[iter_graph].tensor_parm.tensor_str.str_w;
+    graph->nodes[iter_graph].tensor_parm.tensor_str.str_w = 1.0;
+  }
+
   std::vector<int32_t> s_empty = {};
-  strategies.push_back(*s);
   strategies.push_back(s_empty);
   strategies.push_back(s_empty);
   return strategies;
 }
 
-std::vector<std::vector<int32_t>> PrepareGatherV2(const std::shared_ptr<std::vector<int32_t>> &s) {
+std::vector<std::vector<int32_t>> PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                  const size_t iter_ops, std::vector<int32_t> s) {
   std::vector<std::vector<int32_t>> strategies;
-  strategies.push_back(*s);
+
+  int32_t axis = 0;
+  auto axis_input = GetValue<int>(ops[iter_ops]->input_value().at(2));
+  if (axis_input < 0) {
+    axis_input += SizeToInt(ops[iter_ops]->inputs_tensor_info()[0].shape().size());
+  }
+  axis = axis_input;
+  if (axis >= SizeToInt(s.size())) {
+    MS_LOG(EXCEPTION) << "Failure: GatherV2' axis out of range.";
+  }
+  s[axis] = 1;
+  strategies.push_back(s);
+
+  auto pos = ops[iter_ops]->name().find("Info");
+  auto name = ops[iter_ops]->name().substr(0, pos);
+  if (name == "GatherV2") {
+    return strategies;
+  }
+
+  std::vector<int32_t> s_indices;
+  for (size_t i = 0; i < ops[iter_ops]->inputs_tensor_info()[1].shape().size(); i++) {
+    s_indices.push_back(1);
+  }
+  strategies.push_back(s_indices);
+
+  return strategies;
+}
+
+std::vector<std::vector<int32_t>> PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t iter_ops, std::vector<int32_t> s) {
+  int32_t axis = 0;
+  auto iter = ops[iter_ops]->attrs().find(AXIS);
+  if (iter != ops[iter_ops]->attrs().end()) {
+    MS_EXCEPTION_IF_NULL(iter->second);
+    if (iter->second->isa<Int32Imm>()) {
+      axis = iter->second->cast<Int32ImmPtr>()->value();
+    } else {
+      MS_LOG(EXCEPTION) << ops[iter_ops]->name() << " : The value of axis is not int.";
+    }
+  }
+
+  int32_t axis_index = axis;
+  if (axis < 0) {
+    size_t input_dim = ops[iter_ops]->inputs_tensor_info()[0].shape().size();
+    axis_index = static_cast<int32_t>(input_dim) + axis;
+  }
+
+  s[IntToSize(axis_index)] = 1;
+
+  std::vector<std::vector<int32_t>> strategies;
+  strategies.push_back(s);
   return strategies;
 }
 
@@ -209,7 +260,7 @@ std::vector<std::vector<int32_t>> MakeRecSearchStrategy(const std::shared_ptr<Gr
     } else if (output_size == 0) {
       s = {};
     } else {
-      MS_LOG(ERROR) << "Tensor's output size is unexcepted.";
+      MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Tensor's output size is unexcepted.";
     }
     strategies.push_back(s);
   }
@@ -229,7 +280,7 @@ std::vector<std::vector<int32_t>> MakeDataParallelStrategy(const std::shared_ptr
   StrategyPtr origin_strategy = ops[iter_ops]->strategy();
   std::vector<std::vector<int32_t>> strategies;
   size_t max_device_num = g_device_manager->DeviceNum();
-  size_t target_tensor_batch = ops[iter_ops]->outputs_tensor_info()[0].shape()[0];
+  size_t target_tensor_batch = ops[iter_ops]->inputs_tensor_info()[0].shape()[0];
   for (size_t iter_op_inputs = 0; iter_op_inputs < ops[iter_ops]->inputs_tensor_info().size(); iter_op_inputs++) {
     if (iter_op_inputs >= origin_strategy->GetInputDim().size()) {
       MS_LOG(EXCEPTION) << "Failure: Strategy's InputDim out of range.";
@@ -244,8 +295,10 @@ std::vector<std::vector<int32_t>> MakeDataParallelStrategy(const std::shared_ptr
         } else {
           s.push_back(1);
         }
+      } else if (input_size == 0) {
+        s = {};
       } else {
-        MS_LOG(ERROR) << "Tensor's shape is unknown.";
+        MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Tensor's shape is unknown.";
       }
     }
     strategies.push_back(s);
@@ -285,25 +338,20 @@ std::vector<std::vector<int32_t>> PrepareStrategy(const std::shared_ptr<Graph> &
 
   if (type == MATMUL) {
     return PrepareMatMul(graph, ops, iter_graph, iter_ops);
-  } else if (type == PRELU) {
-    return PreparePReLU(graph, ops, iter_graph, iter_ops);
-  } else if (type == BATCH_NORM) {
-    return PrepareBatchNorm(graph, ops, iter_graph, iter_ops);
-  } else if (type == SOFTMAX || type == LOG_SOFTMAX || type == SPARSE_SOFTMAX_CROSS_ENTROPY_WITH_LOGITS ||
-             type == SOFTMAX_CROSS_ENTROPY_WITH_LOGITS) {
-    return MakeDataParallelStrategy(graph, ops, iter_graph, iter_ops);
+  } else if (type == ONEHOT) {
+    return PrepareOneHot(graph, ops, iter_graph, iter_ops);
   } else {
     return MakeRecSearchStrategy(graph, ops, iter_graph, iter_ops);
   }
 }
 
-void GeneratePartitionedOperatorStrategy(const std::shared_ptr<Graph> graph,
+void GeneratePartitionedOperatorStrategy(const std::shared_ptr<Graph> &graph,
                                          const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                         const std::shared_ptr<std::vector<size_t>> index_list) {
+                                         const std::shared_ptr<std::vector<size_t>> &index_list) {
   for (size_t iter_ops = 0; iter_ops < (size_t)index_list->size(); iter_ops++) {
     std::vector<std::vector<int32_t>> strategies;
     size_t iter_graph = index_list->at(iter_ops);
-    if (iter_graph != SIZE_MAX) {
+    if (iter_graph != SIZE_MAX && ops[iter_ops]->type() != GET_NEXT) {
       strategies = PrepareStrategy(graph, ops, iter_graph, iter_ops);
     }
     StrategyPtr sp = std::make_shared<Strategy>(0, strategies);
@@ -328,7 +376,7 @@ size_t FindIndexOfOperatorIncoming(const std::vector<std::vector<std::string>> &
   return incoming_op_index;
 }
 
-std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> graph,
+std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &graph,
                                                         const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                         const size_t iter_ops, const size_t iter_graph) {
   std::vector<int32_t> s;
@@ -348,7 +396,7 @@ std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Gr
       s.push_back(1 / graph->nodes[iter_graph].tensor_parm.tensor_str.str_h);
       s.push_back(1 / graph->nodes[iter_graph].tensor_parm.tensor_str.str_w);
     } else {
-      MS_LOG(ERROR) << "Tensor's shape is unknown.";
+      MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Tensor's shape is unknown.";
     }
     break;
   }
@@ -358,7 +406,8 @@ std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Gr
 std::vector<int32_t> PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                           const size_t incoming_op_index) {
   std::vector<int32_t> s;
-  if (ops[incoming_op_index]->type() == RESHAPE || ops[incoming_op_index]->type() == GATHERV2) {
+  if (ops[incoming_op_index]->type() == RESHAPE || ops[incoming_op_index]->type() == GATHERV2 ||
+      ops[incoming_op_index]->type() == TRANSPOSE) {
     return s;
   }
   auto strategy = ops[incoming_op_index]->selected_strategy();
@@ -426,13 +475,23 @@ std::vector<int32_t> ModifyStrategyIfSqueezeIncoming(const std::vector<std::shar
   return s_Squeeze;
 }
 
+bool GetKeepDims(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
+  bool keepdims = false;
+  auto keep_dims_iter = ops[iter_ops]->attrs().find(KEEP_DIMS);
+  if (keep_dims_iter == ops[iter_ops]->attrs().end()) {
+    MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Don't have attr keep_dims.";
+  }
+  MS_EXCEPTION_IF_NULL(keep_dims_iter->second);
+  if (!keep_dims_iter->second->isa<BoolImm>()) {
+    MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Keep_dims is not a bool.";
+  }
+  keepdims = keep_dims_iter->second->cast<BoolImmPtr>()->value();
+  return keepdims;
+}
+
 std::vector<int32_t> GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
   std::vector<int32_t> dim_list;
-  bool keep_dims;
-  if (!ops[iter_ops]->attrs().find(KEEP_DIMS)->second->isa<BoolImm>()) {
-    MS_LOG(EXCEPTION) << "Failure: Parameter keep_dims is not a boolean value." << std::endl;
-  }
-  keep_dims = ops[iter_ops]->attrs().find(KEEP_DIMS)->second->cast<BoolImmPtr>()->value();
+  bool keep_dims = GetKeepDims(ops, iter_ops);
   if (keep_dims != false) {
     return dim_list;
   }
@@ -478,6 +537,62 @@ std::vector<int32_t> ModifyStrategyIfReduceIncoming(const std::vector<std::share
   return s_Reduce;
 }
 
+std::vector<int32_t> GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops) {
+  std::vector<int32_t> dim_list;
+  auto iter = ops[iter_ops]->attrs().find(AXIS);
+  if (iter == ops[iter_ops]->attrs().end()) {
+    MS_LOG(EXCEPTION) << ops[iter_ops]->name() << ": Don't have attr axis.";
+  }
+  auto input_dim = ops[iter_ops]->inputs_tensor_info()[0].shape().size();
+  MS_EXCEPTION_IF_NULL(iter->second);
+  if (iter->second->isa<ValueTuple>()) {
+    auto attr_axis = GetValue<std::vector<int>>(iter->second);
+    if (attr_axis.empty()) {
+      for (size_t i = 0; i < input_dim; ++i) {
+        dim_list.push_back(SizeToInt(i));
+      }
+    } else {
+      for (auto &axis : attr_axis) {
+        axis < 0 ? dim_list.push_back(axis + SizeToInt(input_dim)) : dim_list.push_back(axis);
+      }
+    }
+  } else if (iter->second->isa<Int32Imm>()) {
+    int axis = GetValue<int>(iter->second);
+    axis < 0 ? dim_list.push_back(axis + SizeToInt(input_dim)) : dim_list.push_back(axis);
+  } else {
+    MS_LOG(EXCEPTION) << "Axis type is invalid.";
+  }
+  return dim_list;
+}
+
+std::vector<int32_t> ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t incoming_op_index, std::vector<int32_t> s) {
+  bool keepdims = GetKeepDims(ops, incoming_op_index);
+  if (keepdims) {
+    return s;
+  }
+
+  std::vector<int32_t> s_Arg;
+  std::vector<int32_t> axis_list;
+  for (size_t i = 0; i < s.size(); i++) {
+    axis_list.push_back(i);
+  }
+
+  auto dim_list = GetDimListFromAttrs(ops, incoming_op_index);
+  for (auto axis : dim_list) {
+    auto it = find(axis_list.begin(), axis_list.end(), axis);
+    if (it == axis_list.end()) {
+      MS_LOG(EXCEPTION) << "Failure: Can not find dimension indexes in Axis." << std::endl;
+    }
+    axis_list.erase(it);
+  }
+
+  for (size_t i = 0; i < (size_t)axis_list.size(); i++) {
+    s_Arg.push_back(s[axis_list[i]]);
+  }
+  return s_Arg;
+}
+
 std::vector<int32_t> CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                        const size_t iter_ops, const size_t incoming_op_index) {
   std::vector<int32_t> s;
@@ -490,6 +605,9 @@ std::vector<int32_t> CopyIncomingOperatorInputStrategy(const std::vector<std::sh
         ops[incoming_op_index]->type() == REDUCE_MIN || ops[incoming_op_index]->type() == REDUCE_MEAN) {
       s = ModifyStrategyIfReduceIncoming(ops, incoming_op_index, s);
     }
+    if (ops[incoming_op_index]->type() == ARGMAXWITHVALUE || ops[incoming_op_index]->type() == ARGMINWITHVALUE) {
+      s = ModifyStrategyIfArgIncoming(ops, incoming_op_index, s);
+    }
   }
   return s;
 }
@@ -513,11 +631,11 @@ std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vect
   if (ops[iter_ops]->type() == BIAS_ADD) {
     return PrepareBiasAdd(s_ptr);
   }
-  if (ops[iter_ops]->type() == ONEHOT) {
-    return PrepareOneHot(s_ptr);
-  }
   if (ops[iter_ops]->type() == GATHERV2) {
-    return PrepareGatherV2(s_ptr);
+    return PrepareGatherV2(ops, iter_ops, basic_stra);
+  }
+  if (ops[iter_ops]->type() == L2_NORMALIZE) {
+    return PrepareL2Normalize(ops, iter_ops, basic_stra);
   }
 
   for (size_t iter_op_inputs = 0; iter_op_inputs < (size_t)ops[iter_ops]->inputs_tensor_info().size();
@@ -544,11 +662,11 @@ std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vect
   return stra;
 }
 
-void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> graph,
+void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> &graph,
                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                const std::vector<std::vector<std::string>> &input_tensor_names,
-                                               const std::shared_ptr<std::vector<size_t>> index_list,
-                                               const std::shared_ptr<std::vector<size_t>> no_stra_op_list) {
+                                               const std::shared_ptr<std::vector<size_t>> &index_list,
+                                               const std::shared_ptr<std::vector<size_t>> &no_stra_op_list) {
   if (no_stra_op_list->size() == 0) {
     return;
   }
@@ -559,7 +677,7 @@ void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> grap
     std::vector<std::vector<int32_t>> stra;
     std::vector<int32_t> s;
     size_t incoming_op_index = FindIndexOfOperatorIncoming(input_tensor_names, iter_ops);
-    if (incoming_op_index != SIZE_MAX && ops[iter_ops]->type() != ONEHOT) {
+    if (incoming_op_index != SIZE_MAX) {
       auto iter_graph = index_list->at(incoming_op_index);
       if (iter_graph != SIZE_MAX) {
         s = CopyIncomingOperatorOutputStrategy(graph, ops, iter_ops, iter_graph);
@@ -617,7 +735,8 @@ std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::sh
   std::vector<int32_t> s;
   if (ops[iter_ops]->type() == REDUCE_MAX || ops[iter_ops]->type() == REDUCE_MIN ||
       ops[iter_ops]->type() == REDUCE_SUM || ops[iter_ops]->type() == REDUCE_MEAN || ops[iter_ops]->type() == RESHAPE ||
-      ops[iter_ops]->type() == GATHERV2) {
+      ops[iter_ops]->type() == GATHERV2 || ops[iter_ops]->type() == TRANSPOSE ||
+      ops[iter_ops]->type() == ARGMAXWITHVALUE || ops[iter_ops]->type() == ARGMINWITHVALUE) {
     return s;
   }
 
@@ -640,7 +759,7 @@ std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::sh
   }
 
   if (outgoing_op_index != SIZE_MAX && iter_op_inputs != SIZE_MAX) {
-    for (size_t k = 0; k < ops[outgoing_op_index]->selected_strategy()->GetInputDim()[iter_op_inputs].size(); ++k) {
+    for (size_t k = 0; k < ops[iter_ops]->outputs_tensor_info()[0].shape().size(); ++k) {
       s.push_back(ops[outgoing_op_index]->selected_strategy()->GetInputDim()[iter_op_inputs][k]);
     }
   }
@@ -649,7 +768,7 @@ std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::sh
 
 void GenerateEliminatedOperatorStrategyBackward(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                 const std::vector<std::vector<std::string>> &input_tensor_names,
-                                                const std::shared_ptr<std::vector<size_t>> no_stra_op_list) {
+                                                const std::shared_ptr<std::vector<size_t>> &no_stra_op_list) {
   if (no_stra_op_list->size() == 0) {
     return;
   }
@@ -679,16 +798,16 @@ void GenerateEliminatedOperatorStrategyBackward(const std::vector<std::shared_pt
   }
 }
 
-void GenerateRemainingOperatorStrategy(const std::shared_ptr<Graph> graph,
+void GenerateRemainingOperatorStrategy(const std::shared_ptr<Graph> &graph,
                                        const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                        const std::vector<std::vector<std::string>> &input_tensor_names,
-                                       const std::shared_ptr<std::vector<size_t>> index_list,
-                                       const std::shared_ptr<std::vector<size_t>> no_stra_op_list) {
+                                       const std::shared_ptr<std::vector<size_t>> &index_list,
+                                       const std::shared_ptr<std::vector<size_t>> &no_stra_op_list) {
   if (no_stra_op_list->size() == 0) {
     return;
   }
 
-  size_t no_stra_op_list_size;
+  size_t no_stra_op_list_size = no_stra_op_list->size();
   do {
     no_stra_op_list_size = no_stra_op_list->size();
     GenerateEliminatedOperatorStrategyForward(graph, ops, input_tensor_names, index_list, no_stra_op_list);

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_generate_strategy.h

@@ -27,22 +27,21 @@
 
 namespace mindspore {
 namespace parallel {
-void GenerateStrategy(std::shared_ptr<Graph> graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                      const std::shared_ptr<std::vector<std::vector<size_t>>> eli_list,
+void GenerateStrategy(const std::shared_ptr<Graph> &graph, const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                      const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list,
                       const std::vector<std::vector<std::string>> &input_tensor_names,
-                      const std::shared_ptr<std::vector<size_t>> index_list);
+                      const std::shared_ptr<std::vector<size_t>> &index_list);
 std::vector<std::vector<int32_t>> PrepareMatMul(const std::shared_ptr<Graph> &graph,
                                                 const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                 const size_t iter_graph, const size_t iter_ops);
-std::vector<std::vector<int32_t>> PreparePReLU(const std::shared_ptr<Graph> &graph,
-                                               const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                               const size_t iter_graph, const size_t iter_ops);
-std::vector<std::vector<int32_t>> PrepareBatchNorm(const std::shared_ptr<Graph> &graph,
-                                                   const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                                   const size_t iter_graph, const size_t iter_ops);
 std::vector<std::vector<int32_t>> PrepareBiasAdd(const std::shared_ptr<std::vector<int32_t>> &s);
-std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<std::vector<int32_t>> &s);
-std::vector<std::vector<int32_t>> PrepareGatherV2(const std::shared_ptr<std::vector<int32_t>> &s);
+std::vector<std::vector<int32_t>> PrepareOneHot(const std::shared_ptr<Graph> &graph,
+                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                const size_t iter_graph, const size_t iter_ops);
+std::vector<std::vector<int32_t>> PrepareGatherV2(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                  const size_t iter_ops, std::vector<int32_t> s);
+std::vector<std::vector<int32_t>> PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                     const size_t iter_ops, std::vector<int32_t> s);
 std::vector<std::vector<int32_t>> MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
                                                         const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                         const size_t iter_graph, const size_t iter_ops);
@@ -52,12 +51,12 @@ std::vector<std::vector<int32_t>> MakeDataParallelStrategy(const std::shared_ptr
 std::vector<std::vector<int32_t>> PrepareStrategy(const std::shared_ptr<Graph> &graph,
                                                   const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                   const size_t iter_graph, const size_t iter_ops);
-void GeneratePartitionedOperatorStrategy(const std::shared_ptr<Graph> graph,
+void GeneratePartitionedOperatorStrategy(const std::shared_ptr<Graph> &graph,
                                          const std::vector<std::shared_ptr<OperatorInfo>> &ops,
-                                         const std::shared_ptr<std::vector<size_t>> index_list);
+                                         const std::shared_ptr<std::vector<size_t>> &index_list);
 size_t FindIndexOfOperatorIncoming(const std::vector<std::vector<std::string>> &input_tensor_names,
                                    const size_t iter_ops);
-std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> graph,
+std::vector<int32_t> CopyIncomingOperatorOutputStrategy(const std::shared_ptr<Graph> &graph,
                                                         const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                         const size_t iter_ops, const size_t iter_graph);
 std::vector<int32_t> PrepareIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
@@ -65,19 +64,23 @@ std::vector<int32_t> PrepareIncomingOperatorInputStrategy(const std::vector<std:
 std::vector<int32_t> GetAxisList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const int iter_ops);
 std::vector<int32_t> ModifyStrategyIfSqueezeIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                      const size_t incoming_op_index, std::vector<int32_t> s);
+bool GetKeepDims(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
 std::vector<int32_t> GetDimList(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
 std::vector<int32_t> ModifyStrategyIfReduceIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                     const size_t incoming_op_index, std::vector<int32_t> s);
+std::vector<int32_t> GetDimListFromAttrs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops);
+std::vector<int32_t> ModifyStrategyIfArgIncoming(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t incoming_op_index, std::vector<int32_t> s);
 std::vector<int32_t> CopyIncomingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                        const size_t iter_ops, const size_t incoming_op_index);
 std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                                  const size_t iter_ops,
                                                                  std::vector<int32_t> basic_stra);
-void GenerateEliminatedOperatorStrategyForward(std::shared_ptr<Graph> graph,
+void GenerateEliminatedOperatorStrategyForward(const std::shared_ptr<Graph> &graph,
                                                const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                const std::vector<std::vector<std::string>> &input_tensor_names,
-                                               const std::shared_ptr<std::vector<size_t>> index_list,
-                                               const std::shared_ptr<std::vector<size_t>> no_stra_op_list);
+                                               const std::shared_ptr<std::vector<size_t>> &index_list,
+                                               const std::shared_ptr<std::vector<size_t>> &no_stra_op_list);
 std::vector<int32_t> ModifyStrategyIfSqueezeOutgoing(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                      const size_t iter_ops, std::vector<int32_t> s);
 std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
@@ -85,12 +88,12 @@ std::vector<int32_t> CopyOutgoingOperatorInputStrategy(const std::vector<std::sh
                                                        const size_t iter_ops);
 void GenerateEliminatedOperatorStrategyBackward(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                 const std::vector<std::vector<std::string>> &input_tensor_names,
-                                                const std::shared_ptr<std::vector<size_t>> no_stra_op_list);
-void GenerateRemainingOperatorStrategy(const std::shared_ptr<Graph> graph,
+                                                const std::shared_ptr<std::vector<size_t>> &no_stra_op_list);
+void GenerateRemainingOperatorStrategy(const std::shared_ptr<Graph> &graph,
                                        const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                        const std::vector<std::vector<std::string>> &input_tensor_names,
-                                       const std::shared_ptr<std::vector<size_t>> index_list,
-                                       const std::shared_ptr<std::vector<size_t>> no_stra_op_list);
+                                       const std::shared_ptr<std::vector<size_t>> &index_list,
+                                       const std::shared_ptr<std::vector<size_t>> &no_stra_op_list);
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // PARALLEL_AUTO_PARALLEL_REC_GENERATE_STRATEGY_H_

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_graph.h

@@ -38,6 +38,7 @@ enum OperatorType {
   kRecBiasAdd,
   kRecSoftmax,
   kRecSparseSoftmaxCrossEntropyWithLogits,
+  kRecSoftmaxCrossEntropyWithLogits,
   kRecOneHot,
   kRecLog,
   kRecExp,
@@ -49,7 +50,8 @@ enum OperatorType {
   kRecCast,
   kRecReduce,
   kRecPReLU,
-  kRecGatherV2
+  kRecGatherV2,
+  kRecArgWithValue
 };
 
 enum InfoType { kApplication, kConstant };

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.cc

@@ -40,7 +40,7 @@ const TensorParam MakeTensor(int n, int c, int h, int w) {
   return tensor;
 }
 
-Graph::NodeType MakeNewOperator(std::vector<std::shared_ptr<OperatorInfo>> ops, size_t iter_ops) {
+Graph::NodeType MakeNewOperator(const std::vector<std::shared_ptr<OperatorInfo>> &ops, size_t iter_ops) {
   Graph::NodeType NewOp;
   NewOp.name = ops[iter_ops]->name();
   NewOp.info = InfoType::kApplication;
@@ -140,7 +140,7 @@ std::shared_ptr<Graph> ParseGraph(const std::vector<std::shared_ptr<OperatorInfo
   return graph;
 }
 
-void MakeEdge(const std::vector<std::vector<std::string>> &input_tensor_names, std::shared_ptr<Graph> graph) {
+void MakeEdge(const std::vector<std::vector<std::string>> &input_tensor_names, const std::shared_ptr<Graph> &graph) {
   for (size_t iter_i = 0; iter_i < input_tensor_names.size(); iter_i++) {
     for (size_t iter_j = 1; iter_j < input_tensor_names[iter_i].size(); iter_j++) {
       size_t head_node_index = GetIndexInInputTensorNames(input_tensor_names, input_tensor_names[iter_i][iter_j]);
@@ -163,8 +163,8 @@ size_t GetIndexInInputTensorNames(const std::vector<std::vector<std::string>> &i
   return SIZE_MAX;
 }
 
-void Eliminate_Aux(const size_t node_index, const std::shared_ptr<Graph> graph,
-                   const std::shared_ptr<std::vector<std::vector<size_t>>> eli_list) {
+void Eliminate_Aux(const size_t node_index, const std::shared_ptr<Graph> &graph,
+                   const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list) {
   std::vector<size_t> eli;
   eli.push_back(node_index);
   for (size_t i = 0; i < (size_t)graph->nodes[node_index].node_out.size(); i++) {
@@ -211,18 +211,18 @@ void Eliminate_Aux(const size_t node_index, const std::shared_ptr<Graph> graph,
   }
 }
 
-std::shared_ptr<Graph> EliminateGraph(const std::shared_ptr<Graph> graph,
-                                      const std::shared_ptr<std::vector<std::vector<size_t>>> eli_list,
-                                      const std::shared_ptr<std::vector<size_t>> index_list) {
+std::shared_ptr<Graph> EliminateGraph(const std::shared_ptr<Graph> &graph,
+                                      const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list,
+                                      const std::shared_ptr<std::vector<size_t>> &index_list) {
   MS_EXCEPTION_IF_NULL(graph);
-  const std::set<OperatorType> type_list = {
-    OperatorType::kRecOneHot, OperatorType::kRecReLU,      OperatorType::kRecLog,     OperatorType::kRecExp,
-    OperatorType::kRecAdd,    OperatorType::kRecElmWiseOp, OperatorType::kRecBiasAdd, OperatorType::kRecSub,
-    OperatorType::kRecMul,    OperatorType::kRecDiv,       OperatorType::kRecSqueeze, OperatorType::kRecReduce,
-    OperatorType::kRecCast,   OperatorType::kRecReshape,   OperatorType::kRecGatherV2};
+  static const std::set<OperatorType> elementwise_type = {
+    OperatorType::kRecReLU,      OperatorType::kRecLog,      OperatorType::kRecExp,         OperatorType::kRecAdd,
+    OperatorType::kRecElmWiseOp, OperatorType::kRecBiasAdd,  OperatorType::kRecSub,         OperatorType::kRecMul,
+    OperatorType::kRecDiv,       OperatorType::kRecSqueeze,  OperatorType::kRecReduce,      OperatorType::kRecCast,
+    OperatorType::kRecReshape,   OperatorType::kRecGatherV2, OperatorType::kRecArgWithValue};
   for (size_t node_index = 0; node_index < (size_t)graph->nodes.size(); node_index++) {
     auto type = graph->nodes[node_index].apply.op_type;
-    if (type_list.find(type) != type_list.end()) {
+    if (elementwise_type.find(type) != elementwise_type.end()) {
       Eliminate_Aux(node_index, graph, eli_list);
     }
   }
@@ -250,12 +250,22 @@ std::shared_ptr<Graph> EliminateGraph(const std::shared_ptr<Graph> graph,
 
     new_graph->nodes.push_back(graph->nodes[i]);
     auto *node_in = &new_graph->nodes[index_list->at(i)].node_in;
-    for (size_t j = 0; j < node_in->size(); j++) {
-      node_in->at(j) = index_list->at(node_in->at(j));
+    for (size_t j = node_in->size(); j > 0; j--) {
+      bool IsEliminated = (index_list->at(node_in->at(j - 1)) == SIZE_MAX);
+      if (IsEliminated) {
+        node_in->erase(node_in->begin() + j - 1);
+      } else {
+        node_in->at(j - 1) = index_list->at(node_in->at(j - 1));
+      }
     }
     auto *node_out = &new_graph->nodes[index_list->at(i)].node_out;
-    for (size_t j = 0; j < node_out->size(); j++) {
-      node_out->at(j) = index_list->at(node_out->at(j));
+    for (size_t j = node_out->size(); j > 0; j--) {
+      bool IsEliminated = (index_list->at(node_out->at(j - 1)) == SIZE_MAX);
+      if (IsEliminated) {
+        node_out->erase(node_out->begin() + j - 1);
+      } else {
+        node_out->at(j - 1) = index_list->at(node_out->at(j - 1));
+      }
     }
   }
   return new_graph;

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_parse_graph.h

@@ -47,6 +47,8 @@ const std::map<std::string, OperatorType> DictOpType{
   {REDUCE_MIN, OperatorType::kRecReduce},
   {REDUCE_MEAN, OperatorType::kRecReduce},
   {GATHERV2, OperatorType::kRecGatherV2},
+  {ARGMAXWITHVALUE, OperatorType::kRecArgWithValue},
+  {ARGMINWITHVALUE, OperatorType::kRecArgWithValue},
 
   {RELU, OperatorType::kRecReLU},
   {"ReLU6", OperatorType::kRecReLU},
@@ -59,6 +61,7 @@ const std::map<std::string, OperatorType> DictOpType{
 
   {PRELU, OperatorType::kRecPReLU},
 
+  {TRANSPOSE, OperatorType::kRecElmWiseOp},
   {L2_NORMALIZE, OperatorType::kRecElmWiseOp},
   {TENSOR_ADD, OperatorType::kRecElmWiseOp},
   {SUB, OperatorType::kRecElmWiseOp},
@@ -67,7 +70,7 @@ const std::map<std::string, OperatorType> DictOpType{
   {REAL_DIV, OperatorType::kRecElmWiseOp},
   {SOFTMAX, OperatorType::kRecSoftmax},
   {LOG_SOFTMAX, OperatorType::kRecSoftmax},
-  {SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, OperatorType::kRecSoftmax},
+  {SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, OperatorType::kRecSoftmaxCrossEntropyWithLogits},
   {SQRT, OperatorType::kRecElmWiseOp},
   {NEG, OperatorType::kRecElmWiseOp},
   {POW, OperatorType::kRecElmWiseOp},
@@ -107,7 +110,7 @@ const std::map<std::string, OperatorType> DictOpType{
 
 const TensorParam MakeTensor(int n, int c, int h, int w);
 
-Graph::NodeType MakeNewOperator(std::vector<std::shared_ptr<OperatorInfo>> ops, size_t iter_ops);
+Graph::NodeType MakeNewOperator(const std::vector<std::shared_ptr<OperatorInfo>> &ops, size_t iter_ops);
 
 OperatorRec CompleteOperatorInputs(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
                                    Graph::NodeType NewTensor);
@@ -118,17 +121,17 @@ TensorParam Complete2DInputs(const std::vector<std::shared_ptr<OperatorInfo>> &o
 std::shared_ptr<Graph> ParseGraph(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                   const std::vector<std::vector<std::string>> &input_tensor_names);
 
-void MakeEdge(const std::vector<std::vector<std::string>> &input_tensor_names, std::shared_ptr<Graph> graph);
+void MakeEdge(const std::vector<std::vector<std::string>> &input_tensor_names, const std::shared_ptr<Graph> &graph);
 
 size_t GetIndexInInputTensorNames(const std::vector<std::vector<std::string>> &input_tensor_names,
                                   const std::string &input_name);
 
-void Eliminate_Aux(const size_t node_index, const std::shared_ptr<Graph> graph,
-                   const std::shared_ptr<std::vector<std::vector<size_t>>> eli_list);
+void Eliminate_Aux(const size_t node_index, const std::shared_ptr<Graph> &graph,
+                   const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list);
 
-std::shared_ptr<Graph> EliminateGraph(const std::shared_ptr<Graph> graph,
-                                      const std::shared_ptr<std::vector<std::vector<size_t>>> eli_list,
-                                      const std::shared_ptr<std::vector<size_t>> index_list);
+std::shared_ptr<Graph> EliminateGraph(const std::shared_ptr<Graph> &graph,
+                                      const std::shared_ptr<std::vector<std::vector<size_t>>> &eli_list,
+                                      const std::shared_ptr<std::vector<size_t>> &index_list);
 }  // namespace parallel
 }  // namespace mindspore
 #endif  // PARALLEL_AUTO_PARALLEL_REC_PARSE_GRAPH_H_

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.cc

@@ -68,19 +68,24 @@ double GetWeights(const Graph::NodeType &node) {
     auto cost_ptr = std::make_shared<CostBiasAdd>();
 
     return cost_ptr->GetMinCostIn();
-  } else if (op.op_type == OperatorType::kRecOneHot || op.op_type == OperatorType::kRecLog ||
-             op.op_type == OperatorType::kRecExp || op.op_type == OperatorType::kRecAdd ||
-             op.op_type == OperatorType::kRecSub || op.op_type == OperatorType::kRecMul ||
-             op.op_type == OperatorType::kRecDiv || op.op_type == OperatorType::kRecSqueeze ||
-             op.op_type == OperatorType::kRecCast) {
+  } else if (op.op_type == OperatorType::kRecLog || op.op_type == OperatorType::kRecExp ||
+             op.op_type == OperatorType::kRecAdd || op.op_type == OperatorType::kRecSub ||
+             op.op_type == OperatorType::kRecMul || op.op_type == OperatorType::kRecDiv ||
+             op.op_type == OperatorType::kRecSqueeze || op.op_type == OperatorType::kRecCast) {
     // For element-wise op
     auto cost_ptr = std::make_shared<CostCommon>();
 
     return cost_ptr->GetMinCostIn();
-  } else if (op.op_type == OperatorType::kRecUnkownType || op.op_type == OperatorType::kRecPReLU ||
-             op.op_type == OperatorType::kRecBatchNorm || op.op_type == OperatorType::kRecSoftmax ||
-             op.op_type == OperatorType::kRecSparseSoftmaxCrossEntropyWithLogits) {
-    // For unprocessed type
+  } else if (op.op_type == OperatorType::kRecBatchNorm || op.op_type == OperatorType::kRecOneHot ||
+             op.op_type == OperatorType::kRecPReLU || op.op_type == OperatorType::kRecSoftmax ||
+             op.op_type == OperatorType::kRecSparseSoftmaxCrossEntropyWithLogits ||
+             op.op_type == OperatorType::kRecSoftmaxCrossEntropyWithLogits) {
+    // For BatchParallel op
+    auto cost_ptr = std::make_shared<CostBatchParallel>();
+
+    return cost_ptr->GetMaxCostIn();
+  } else if (op.op_type == OperatorType::kRecUnkownType) {
+    // For Unkown type
     return 0.0;
   } else {
     MS_LOG(EXCEPTION) << "Failure: GetOperatorWeight failed.";
@@ -88,7 +93,7 @@ double GetWeights(const Graph::NodeType &node) {
 }
 
 // Sort all the nodes by their weights
-std::vector<size_t> SortByWeight(const std::shared_ptr<Graph> graph) {
+std::vector<size_t> SortByWeight(const std::shared_ptr<Graph> &graph) {
   MS_EXCEPTION_IF_NULL(graph);
 
   std::vector<std::pair<double, size_t>> weight_to_node_index;
@@ -119,7 +124,7 @@ std::vector<size_t> SortByWeight(const std::shared_ptr<Graph> graph) {
 // Get optimal strategy to partition the target node
 StrategyRec PartitionNode(const Graph::NodeType &node,
                           const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
-                          std::shared_ptr<Graph> graph) {
+                          const std::shared_ptr<Graph> &graph) {
   bool enable_conv_chw_partition = false;
   MS_EXCEPTION_IF_NULL(graph);
 
@@ -158,19 +163,26 @@ StrategyRec PartitionNode(const Graph::NodeType &node,
     auto cost_ptr = std::make_shared<CostBiasAdd>();
 
     return cost_ptr->GetOptimalStr(node, node_name_to_strategy, *graph);
-  } else if (node.apply.op_type == OperatorType::kRecOneHot || node.apply.op_type == OperatorType::kRecLog ||
-             node.apply.op_type == OperatorType::kRecExp || node.apply.op_type == OperatorType::kRecAdd ||
-             node.apply.op_type == OperatorType::kRecSub || node.apply.op_type == OperatorType::kRecMul ||
-             node.apply.op_type == OperatorType::kRecDiv || node.apply.op_type == OperatorType::kRecSqueeze ||
-             node.apply.op_type == OperatorType::kRecCast) {
+  } else if (node.apply.op_type == OperatorType::kRecLog || node.apply.op_type == OperatorType::kRecExp ||
+             node.apply.op_type == OperatorType::kRecAdd || node.apply.op_type == OperatorType::kRecSub ||
+             node.apply.op_type == OperatorType::kRecMul || node.apply.op_type == OperatorType::kRecDiv ||
+             node.apply.op_type == OperatorType::kRecSqueeze || node.apply.op_type == OperatorType::kRecCast) {
     // For element-wise op
     auto cost_ptr = std::make_shared<CostCommon>();
 
     return cost_ptr->GetOptimalStr(node, node_name_to_strategy, *graph);
-  } else if (node.apply.op_type == OperatorType::kRecUnkownType || node.apply.op_type == OperatorType::kRecPReLU ||
-             node.apply.op_type == OperatorType::kRecBatchNorm || node.apply.op_type == OperatorType::kRecSoftmax ||
+  } else if (node.apply.op_type == OperatorType::kRecBatchNorm || node.apply.op_type == OperatorType::kRecOneHot ||
+             node.apply.op_type == OperatorType::kRecPReLU || node.apply.op_type == kRecSoftmax ||
              node.apply.op_type == OperatorType::kRecSparseSoftmaxCrossEntropyWithLogits) {
-    // For unprocessed type
+    // For BatchParallel type
+    auto cost_ptr = std::make_shared<CostBatchParallel>();
+    return cost_ptr->GetOptimalStr(node);
+  } else if (node.apply.op_type == OperatorType::kRecSoftmaxCrossEntropyWithLogits) {
+    // For SoftmaxCrossEntropyWithLogits type
+    auto cost_ptr = std::make_shared<CostSoftmaxCrossEntropyWithLogits>();
+    return cost_ptr->GetOptimalStr(node);
+  } else if (node.apply.op_type == OperatorType::kRecUnkownType) {
+    // For Unkown type
     StrategyRec default_strategy;
     return default_strategy;
   } else {
@@ -179,7 +191,8 @@ StrategyRec PartitionNode(const Graph::NodeType &node,
 }
 
 // Parttion graph into all devices.
-Status PartitionForAllDevices(const size_t num_device, const double device_memory, std::shared_ptr<Graph> graph) {
+Status PartitionForAllDevices(const size_t num_device, const double device_memory,
+                              const std::shared_ptr<Graph> &graph) {
   if (num_device < 1) {
     MS_LOG(EXCEPTION) << "ERROR: Number of devices can't be " << num_device << ".";
   }
@@ -249,7 +262,7 @@ Graph::NodeType ApplyStrToTensor(Graph::NodeType Node) {
   return Node;
 }
 
-Status DevicesMemoryControl(const size_t num_device, const double device_memory, std::shared_ptr<Graph> graph) {
+Status DevicesMemoryControl(const size_t num_device, const double device_memory, const std::shared_ptr<Graph> &graph) {
   MS_EXCEPTION_IF_NULL(graph);
   if (num_device == 0) {
     MS_LOG(EXCEPTION) << "Failure: device number is 0.";

mindspore/ccsrc/parallel/auto_parallel/rec_core/rec_partition.h

@@ -32,19 +32,19 @@
 
 namespace mindspore {
 namespace parallel {
-std::vector<size_t> SortByWeight(const std::shared_ptr<Graph> graph);
+std::vector<size_t> SortByWeight(const std::shared_ptr<Graph> &graph);
 
 double GetWeights(const Graph::NodeType &node);
 
 StrategyRec PartitionNode(const Graph::NodeType &node,
                           const std::vector<std::pair<std::string, StrategyRec>> &node_name_to_strategy,
-                          std::shared_ptr<Graph> graph);
+                          const std::shared_ptr<Graph> &graph);
 
-Status PartitionForAllDevices(const size_t num_device, const double device_memory, std::shared_ptr<Graph> graph);
+Status PartitionForAllDevices(const size_t num_device, const double device_memory, const std::shared_ptr<Graph> &graph);
 
 Graph::NodeType ApplyStrToTensor(Graph::NodeType Node);
 
-Status DevicesMemoryControl(const size_t num_device, const double device_memory, std::shared_ptr<Graph> graph);
+Status DevicesMemoryControl(const size_t num_device, const double device_memory, const std::shared_ptr<Graph> &graph);
 
 size_t GetDataTypeSize(const TensorType &type);
 }  // namespace parallel

mindspore/ccsrc/pipeline/action.cc

@@ -282,7 +282,7 @@ bool VmOptimizeAction(const ResourcePtr &res) { return OptimizeAction(res, kVmPa
 
 bool PynativeOptimizeAction(const ResourcePtr &res) { return OptimizeAction(res, kPynativePasses); }
 
-static bool IsCtrlSink(const FuncGraphPtr &graph) {
+static bool IsCtrlSink() {
   auto ms_ctx = MsContext::GetInstance();
   if (ms_ctx->execution_mode() != kGraphMode) {
     return false;
@@ -297,10 +297,9 @@ static bool IsCtrlSink(const FuncGraphPtr &graph) {
     return false;
   }
 
-  if (graph != nullptr && CompileGraphs::ContainMixedTarget(graph)) {
+  if (!ms_ctx->is_multi_graph_sink()) {
     return false;
   }
-
   return true;
 }
 
@@ -310,7 +309,21 @@ bool TaskEmitAction(const ResourcePtr &res) {
   }
   FuncGraphPtr func_graph = res->func_graph();
   auto bc_ptr = res->results()[kBackend].cast<compile::BackendPtr>();
-  if (IsCtrlSink(func_graph)) {
+  auto context_ptr = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(context_ptr);
+  if (CompileGraphs::ContainMixedTarget(func_graph)) {
+    bc_ptr->set_is_multi_graph_sink(false);
+    context_ptr->set_is_multi_graph_sink(false);
+    context_ptr->set_loop_sink_flag(false);
+  } else if (context_ptr->execution_mode() != kPynativeMode) {
+    std::string device_target = context_ptr->device_target();
+    if (device_target == kAscendDevice) {
+      bc_ptr->set_is_multi_graph_sink(true);
+      context_ptr->set_is_multi_graph_sink(true);
+    }
+  }
+
+  if (IsCtrlSink()) {
     res->results()[kOutput] = bc_ptr->CompileGraph(NOT_NULL(func_graph));
     return true;
   }
@@ -318,19 +331,7 @@ bool TaskEmitAction(const ResourcePtr &res) {
   if (bc_ptr->name() == kMsConvert) {
     cut_list = compile::GetMsNonlinearOps();
   }
-
   std::shared_ptr<CompileGraphs> compile = std::make_shared<CompileGraphs>(bc_ptr, cut_list);
-  auto context_ptr = MsContext::GetInstance();
-  MS_EXCEPTION_IF_NULL(context_ptr);
-  if (CompileGraphs::ContainMixedTarget(func_graph)) {
-    bc_ptr->set_is_multi_graph_sink(false);
-    context_ptr->set_loop_sink_flag(false);
-  } else if (context_ptr->execution_mode() != kPynativeMode) {
-    std::string device_target = context_ptr->device_target();
-    if (device_target == kAscendDevice) {
-      bc_ptr->set_is_multi_graph_sink(true);
-    }
-  }
   res->results()[kOutput] = compile->CompileAndLink(func_graph);
   return true;
 }
@@ -340,11 +341,10 @@ bool ExecuteAction(const ResourcePtr &res) {
     MS_LOG(EXCEPTION) << "Execute args error";
   }
 
-  if (IsCtrlSink(nullptr)) {
+  if (IsCtrlSink()) {
     if (!res->results()[kOutput].is<GraphId>()) {
       MS_LOG(EXCEPTION) << "Execute args error";
     }
-
     auto graph_id = res->results()[kOutput].cast<GraphId>();
     std::shared_ptr<compile::Backend> bc_ptr = res->results()[kBackend].cast<std::shared_ptr<compile::Backend>>();
     std::shared_ptr<compile::MsBackend> msbc_ptr = std::dynamic_pointer_cast<compile::MsBackend>(bc_ptr);

mindspore/ccsrc/pipeline/static_analysis/abstract_value.h

@@ -515,11 +515,11 @@ using AbstractNullPtr = std::shared_ptr<AbstractNull>;
 
 class AbstractEllipsis : public AbstractBase {
  public:
-  AbstractEllipsis() : AbstractBase(kEllipsis) { set_type(std::make_shared<Ellipsis>()); }
+  AbstractEllipsis() : AbstractBase(kEllipsis) { set_type(std::make_shared<TypeEllipsis>()); }
   ~AbstractEllipsis() override = default;
   MS_DECLARE_PARENT(AbstractEllipsis, AbstractBase)
 
-  TypePtr BuildType() const override { return std::make_shared<Ellipsis>(); }
+  TypePtr BuildType() const override { return std::make_shared<TypeEllipsis>(); }
   bool operator==(const AbstractEllipsis &other) const;
   bool operator==(const AbstractBase &other) const override;
   AbstractBasePtr Clone() const override { return std::make_shared<AbstractEllipsis>(); }