MKL-DNN LBR-GRU Inference Integration (FP32 LBR-GRU)

.gitmodules

@@ -1,6 +1,3 @@
-[submodule "3rdparty/mshadow"]
-	path = 3rdparty/mshadow
-	url = https://github.com/dmlc/mshadow.git
 [submodule "3rdparty/dmlc-core"]
 	path = 3rdparty/dmlc-core
 	url = https://github.com/dmlc/dmlc-core.git

3rdparty/mshadow/.gitignore

@@ -0,0 +1,21 @@
+# Compiled Object files
+*.slo
+*.lo
+*.o
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*~
+doc/html
+doc/latex
+rabit
+dmlc-core
+*.db
+*.bak
+build

3rdparty/mshadow/.travis.yml

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+# disable sudo to use container based build
+sudo: false
+
+# Use Build Matrix to do lint and build seperately
+env:
+  matrix:
+    - TASK=lint LINT_LANG=cpp
+    - TASK=doc
+    - TASK=build CXX=g++
+
+# dependent apt packages
+addons:
+  apt:
+    packages:
+      - doxygen
+      - wget
+      - unzip
+      - libblas-dev
+      - python3-pip
+
+before_install:
+  - git clone https://github.com/dmlc/dmlc-core
+  - export TRAVIS=dmlc-core/scripts/travis
+  - source ${TRAVIS}/travis_setup_env.sh
+
+install:
+  - pip3 install --upgrade pip --user
+  - pip3 install  --user  cpplint pylint
+
+script: scripts/travis_script.sh
+
+before_cache:
+  - ${TRAVIS}/travis_before_cache.sh
+
+cache:
+  directories:
+    - ${HOME}/.cache/usr
+
+notifications:
+  email:
+    on_success: change
+    on_failure: always
+

3rdparty/mshadow/CHANGES.md

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+Change Log
+=====
+
+mshadow-1.0
+=====
+* Initial release
+
+mshadow-2.0: in progress
+=====
+* Support multiple data type
+* Great refactoring of code
+* Parameter server interface for MultiGPU and distributed learning

3rdparty/mshadow/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.8.7)
+
+project(mshadow C CXX)
+
+set(mshadow_LINT_DIRS mshadow mshadow-ps)
+add_custom_target(mshadow_lint COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DPYTHON_EXECUTABLE=${PYTHON_EXECUTABLE} -DLINT_DIRS=${mshadow_LINT_DIRS} -DPROJECT_SOURCE_DIR=${PROJECT_SOURCE_DIR} -DPROJECT_NAME=mshadow -P ${PROJECT_SOURCE_DIR}/../dmlc-core/cmake/lint.cmake)

3rdparty/mshadow/LICENSE

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+Copyright (c) 2014 by Contributors
+
+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.

3rdparty/mshadow/README.md

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+mshadow: Matrix Shadow
+======
+[![Build Status](https://travis-ci.org/dmlc/mshadow.svg?branch=master)](https://travis-ci.org/dmlc/mshadow)
+
+MShadow is a lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. The goal of mshadow is to support ***efficient***,
+***device invariant*** and ***simple*** tensor library for machine learning project that aims for maximum performance and control, while also emphasize simplicity.
+
+MShadow also provides interface that allows writing Multi-GPU and distributed deep learning programs in an easy and unified way.
+
+* [Contributors](https://github.com/tqchen/mshadow/graphs/contributors)
+* [Tutorial](guide)
+* [Documentation](doc)
+* [Parameter Server Interface for GPU Tensor](guide/mshadow-ps)
+
+Features
+--------
+* Efficient: all the expression you write will be lazily evaluated and compiled into optimized code
+  - No temporal memory allocation will happen for expression you write
+  - mshadow will generate specific kernel for every expression you write in compile time.
+* Device invariant: you can write one code and it will run on both CPU and GPU
+* Simple: mshadow allows you to write machine learning code using expressions.
+* Whitebox: put a float* into the Tensor struct and take the benefit of the package, no memory allocation is happened unless explicitly called
+* Lightweight library: light amount of code to support frequently used functions in machine learning
+* Extendable: user can write simple functions that plugs into mshadow and run on GPU/CPU, no experience in CUDA is required.
+* MultiGPU and Distributed ML: mshadow-ps interface allows user to write efficient MultiGPU and distributed programs in an unified way.
+
+Version
+-------
+* This version mshadow-2.x, there are a lot of changes in the interface and it is not backward compatible with mshadow-1.0
+  - If you use older version of cxxnet, you will need to use the legacy mshadow code
+* For legacy code, refer to [Here](https://github.com/tqchen/mshadow/releases/tag/v1.1)
+* Change log in [CHANGES.md](CHANGES.md)
+
+Projects Using MShadow
+----------------------
+* [MXNet: Efficient and Flexible Distributed Deep Learning Framework](https://github.com/dmlc/mxnet)
+* [CXXNet: A lightweight  C++ based deep learnig framework](https://github.com/dmlc/cxxnet)