Add mm-wg proposal in mep

Signed-off-by: Nleonwanghui <leon.wanghui@huawei.com>

design/meps/mep-mm/MEP-MM.md

+| title | authors | owning-sig | participating-sigs | status | creation-date | reviewers | approvers | stage | milestone |
+| ----- | ------- | ---------- | ------------------ | ------ | ------------- |---------- | --------- | ----- | --------- |
+| MEP-MM | @helloyesterday @jz_90 @yang_lijiang | wg-mm | sig-compiler, sig-executor | provisional | 2020-08-20 | TBD | TBD | NA | beta: "v0.7" |
+
+# MEP-MM: MindSpore Molecular Modeling WG
+
+## Table of Contents
+<!-- toc -->
+- [Summary](#summary)
+- [Motivation](#motivation)
+  - [Goals](#goals)
+  - [Non-Goals](#non-goals)
+- [Proposal](#proposal)
+  - [User Stories](#user-stories)
+  - [Notes/Constraints/Caveats (optional)](#notesconstraintscaveats-optional)
+  - [Risks and Mitigations](#risks-and-mitigations)
+- [Design Details](#design-details)
+  - [Graduation Criteria](#graduation-criteria)
+  - [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy)
+- [Implementation History](#implementation-history)
+- [Drawbacks](#drawbacks)
+- [Alternatives](#alternatives)
+- [Infrastructure Needed (optional)](#infrastructure-needed-optional)
+- [References (optional)](#references-optional)
+<!-- /toc -->
+
+## Summary
+
+MindSpore Molecular Modeling WG aims to build a community collaboration for deep learning framework's application in molecular modeling and simulation.
+
+## Motivation
+
+Deep learning is transforming many areas in science, and it has great potential in
+modeling molecular systems. However, unlike the mature deployment of deep learning in
+computer vision and natural language processing, its development in molecular modeling and
+simulations is still at an early stage, largely because the inductive biases of molecules are
+completely different from those of images or texts.[0]
+
+  <img src="./mm-motivation.png" style="zoom:80%" div align=center/>
+
+### Goals
+
+The goal of the Molecular Modeling WG are as follows:
+- Provide MM specific requirements to related SIGs for each release cycle, and monitoring its implementation via labeled ISSUE/PR.
+- Provide documentations on MM support in MindSpore.
+- Incubate SIG that will be responsible for developing MM specific libs when necessary and approved by TSC.
+
+### Non-Goals
+
+- Full stack MM software implementation is out of scope of this WG.
+
+## Proposal
+
+### User Stories
+
+For deep learning models and algorithms which handle molecules as geometric identities in the 3D Euclidean space, the (fully connected) graph is a reasonable data structure representing the 3D geometry of molecules, and the deep molecular models dealing with the 3D structures are mostly physics-based and can be used to encode or predict configuration-dependent molecular properties.
+
+From a relatively orthogonal viewpoint, the individual molecules (e.g., small organic compounds and structured macromolecules) can also be regarded as other data structures, for instance, sequences (e.g., SMILES134 or sequence of amino acids) or sparse molecular graphs,etc. Deep learning models based on these data structures are mostly information-based, and they can help researchers navigate through the chemical space and change the way of data-mining for cheminformatics. As demonstrated by AlphaFold, we believe that combining the physics-based deep molecular models and the information-based deep learning methods will bring about new solutions to many long-standing problems in physics, chemistry, and biology.
+
+Last but not least, in order to democratize deep learning for molecular modeling and simulations, it is necessary to develop special-purpose hardware and software suitable for fast and userfriendly computation. For example, molecular simulation community will definitely benefit from the auto-differentiation and parallel computation techniques which are the bedrocks of modern large-scale deep learning. Also, as machine learners and molecular simulation practitioners usually work in different platforms, a proper linker or interface which could integrate the molecular modeling software and deep learning software is highly desired. Indeed, researchers from both scientific and business communities like Google are making efforts to this end.
+
+With the improvement of the infrastructure, deep learning is expected to bring a larger impact and more opportunities to molecular modeling and simulations in the near future.
+
+  <img src="./mm-usecase.png" style="zoom:80%" div align=center/>
+
+### Notes/Constraints/Caveats (optional)
+
+NA
+
+### Risks and Mitigations
+
+NA
+
+## Design Details
+
+NA
+
+### Test Plan
+
+NA
+
+### Graduation Criteria
+
+NA
+
+### Upgrade / Downgrade Strategy
+
+NA
+
+## Implementation History
+
+NA
+
+## Drawbacks
+
+NA
+
+## Alternatives
+
+NA
+
+## Infrastructure Needed (optional)
+
+Ascend hardware resources are needed.
+
+## References (optional)
+
+[0] A Perspective on Deep Learning for Molecular Modeling and Simulations. https://dx.doi.org/10.1021/acs.jpca.0c04473

design/meps/MEP-MSLITE.md → design/meps/mep-mslite/MEP-MSLITE.md

-
-| title   | authors                          | owning-sig | participating-sigs | status      | creation-date | reviewers | approvers | stage | milestone     |
-| ------- | -------------------------------- | ---------- | ------------------ | ----------- | ------------- | --------- | --------- | ----- | ------------- |
-| MEP-mslite | @zhengli  @zhiqiangzhai @chaijun | mslite |  | provisional | 2020-08-18    |  | TBD       | beta  | beta : "v0.7" |
-
-# MEP-MSLITE: MindSpore Lite
-
-## Table of Contents
-
-<!-- toc -->
-
-- [MEP-MSLITE: MindSpore Lite](#mep-mslite-mindspore-lite)
-  - [Table of Contents](#table-of-contents)
-  - [Summary](#summary)
-  - [Motivation](#motivation)
-    - [Goals](#goals)
-    - [Non-Goals](#non-goals)
-  - [Proposal](#proposal)
-    - [User Stories](#user-stories)
-      - [Generate a compact target model and low latency and low consumption runtime](#generate-a-compact-target-model-and-low-latency-and-low-consumption-runtime)
-  - [Design Details](#design-details)
-    - [Test Plan](#test-plan)
-  - [Implementation History](#implementation-history)
-  - [Drawbacks](#drawbacks)
-  - [Alternatives](#alternatives)
-  - [References](#references-optional)
-
-  <!-- /toc -->
-## Summary
-MindSpore(MS) Lite is an extremely light-weight deep learning inference framework, 
-and designed for smart-phones and embedded devices, such as watches, headsets, and various IoT devices. 
-It supports Android and iOS, as well as Harmony os, and has industry leading performance. 
-                                
-## Motivation
-Since increased computing power and sensor data, intelligence is moving towards edge devices. Improved AI algorithms are driving the trend towards machine learning be run on the end device, such as smart-phones or automobiles, rather than in the cloud.
-On-device AI can dramatically reduce latency, conserve bandwidth, improve privacy and enable smarter applications.
-
-### Goals
-- Compatibility: supports MindSpore model, as well as mainstream third-party models, such as TensorFlow Lite, Caffe 1.0 and ONNX.
-- High-performance: 
-generates small, low power consumption and fast inference target model for various hardware backends.
-
-- Versatility: supports Harmony, Android and iOS os.
-- Light-weight: small shared library size, should be less than 1MB, and could be easily deployed on 
-resource limited devices. 
-
-### Non-Goals
-- None
-
-## Proposal
-
-MS Lite consists of converter and a runtime library.
-The converter is an offline tool can handle most of the model translation work. 
-The runtime library deploys to device and executes online, 
-it has Lite RT and Lite Micro two modes.
-Lite RT is for slightly resource limited devices, such as smart-phones, 
-while Lite Micro is for extremely resource limited devices, such as watches, headsets. 
-
-- Compatibility
-
-    provides an abundant of operator parsers for MindSpore, TensorFlow Lite, Caffe, ONNX,
-    and supports common neural networks in CV and NLP, 208+ CPU operators, and 60+ GPU operators.
-
-- High performance
-
-    Many optimization methods, including graph optimizations, post training quantization,
-    are applied to model in offline converter, and generated target model is more compact.
-    Graph optimizations, such as operator fusion and constant folding, make model more compact.
-    Post training quantization transfers fp32 model into fix-point int8 model. 
-    It brings nearly 4x smaller model size, low latency and low consumption for inference process. 
-    MS Lite also applies a variety of optimization schemes to NN operations, including using Winograd 
-algorithm in convolution and deconvolution, Strassen algorithm in matrix multiplication.
-Operations support fp64, fp32, fp16 and int8, and are highly optimized with acceleration by 
-neon instructions, hand-written assemble, multi-thread, memory reuse, heterogeneous computing, etc.
-
-- Versatility
-
-    Supports Harmony, iOS and Android os, supports smart-phones, watches, headsets, and various IoT devices.
-
-- Light weight
-
-    MS Lite is highly Optimized under GHLO and GLLO. It has small foot-print, 
-    MS Lite runtime is about 800 kB, and MS Micro is less than 200 KB. 
-    It is flexible and can easily deploy to mobile and a variety of embedded devices.     
-### User Stories
-
-#### Generate a compact target model and low latency and low consumption runtime
-
-Since devices has limited resource with few ROM, RAM, and power, how to deploy AI model to 
-device is very challenge. MS Lite aims to solve the challenge for users, and provides user-friendly, 
-flexible tool to help users to make their own models more slim and more efficiency.
- 
-## Design Details
-
-MS Lite consists of converter and runtime. 
-The converter is an offline tool has three parts, frontend, IR, and backend.
-Runtime deploys to device and executes online.
-
-- **Frontend.** Frontend aims to parse model from MindSpore, TensorFlow Lite, Caffe and ONNX in protobuf.
-- **IR.** IR is to define ANF, including tensor, operations, and graph.
-- **Backend.** Backend is an optimizer based ANF graph, including GHLO, GLLO, and quantization. `GHLO` is short for "graph high level optimization", common optimization methods, such as operators fusion, operator substitution, and constant folding, are included. `GLLO` is short for "graph low level optimization", low level optimization methods are related to hardware, such as layout adjustment, mixed-precision, etc.
-- **Runtime.** Runtime has Lite RT and Lite Micro two modes.
-
-  <img src="./ms-lite-arch.jpg" style="zoom:80%" div align=center/>
-
-### Test Plan
-
-MS Lite employed pytests and nosetest to launch the testing process, 
-and there are two types of testing strategies in MS Lite:
-
-- **Unit Test.** Every operation, optimization or pass in MS has its own unitest. 
-
-- **System test**. The ms Lite module has its own component testing. 
-Basically we classify the testing into compilation verification, 
-function verification and performance testing.
-
-## Implementation History
-- Support high and low level graph optimization.
-- Support post training quantization.
-- Support Arm CPU and Mali GPU.
-- Support fp64, fp32, fp16, int8 operations.
-
-## Drawbacks
-- MS Lite does not support on-device training yet, it is coming soon...
-
-## Alternatives
-- MNN[1], TF Lite[2] and TNN[3] are outstanding on-device AI frameworks. 
-MS Lite is for on-device AI, and MS cloud is for on-cloud AI, 
-both of them are in scope of Huawei's MindSpore AI framework. 
-They share same IR, and optimization passes. MS Lite is more flexible. 
-
-## References
-- [1] https://github.com/alibaba/MNN
-- [2] https://www.tensorflow.org/lite
-- [3] https://github.com/Tencent/TNN
+
+| title   | authors                          | owning-sig | participating-sigs | status      | creation-date | reviewers | approvers | stage | milestone     |
+| ------- | -------------------------------- | ---------- | ------------------ | ----------- | ------------- | --------- | --------- | ----- | ------------- |
+| MEP-mslite | @zhengli  @zhiqiangzhai @chaijun | mslite |  | provisional | 2020-08-18    |  | TBD       | beta  | beta : "v0.7" |
+
+# MEP-MSLITE: MindSpore Lite
+
+## Table of Contents
+
+<!-- toc -->
+
+- [MEP-MSLITE: MindSpore Lite](#mep-mslite-mindspore-lite)
+  - [Table of Contents](#table-of-contents)
+  - [Summary](#summary)
+  - [Motivation](#motivation)
+    - [Goals](#goals)
+    - [Non-Goals](#non-goals)
+  - [Proposal](#proposal)
+    - [User Stories](#user-stories)
+      - [Generate a compact target model and low latency and low consumption runtime](#generate-a-compact-target-model-and-low-latency-and-low-consumption-runtime)
+  - [Design Details](#design-details)
+    - [Test Plan](#test-plan)
+  - [Implementation History](#implementation-history)
+  - [Drawbacks](#drawbacks)
+  - [Alternatives](#alternatives)
+  - [References](#references-optional)
+
+  <!-- /toc -->
+## Summary
+MindSpore(MS) Lite is an extremely light-weight deep learning inference framework, and designed for smart-phones and embedded devices, such as watches, headsets, and various IoT devices.
+It supports Android and iOS, as well as Harmony os, and has industry leading performance.
+
+## Motivation
+Since increased computing power and sensor data, intelligence is moving towards edge devices. Improved AI algorithms are driving the trend towards machine learning be run on the end device, such as smart-phones or automobiles, rather than in the cloud.
+On-device AI can dramatically reduce latency, conserve bandwidth, improve privacy and enable smarter applications.
+
+### Goals
+- Compatibility: supports MindSpore model, as well as mainstream third-party models, such as TensorFlow Lite, Caffe 1.0 and ONNX.
+- High-performance:
+generates small, low power consumption and fast inference target model for various hardware backends.
+
+- Versatility: supports Harmony, Android and iOS os.
+- Light-weight: small shared library size, should be less than 1MB, and could be easily deployed on resource limited devices.
+
+### Non-Goals
+- None
+
+## Proposal
+
+MS Lite consists of converter and a runtime library.
+The converter is an offline tool can handle most of the model translation work.
+The runtime library deploys to device and executes online, it has Lite RT and Lite Micro two modes.
+Lite RT is for slightly resource limited devices, such as smart-phones, while Lite Micro is for extremely resource limited devices, such as watches, headsets.
+
+- Compatibility
+
+    provides an abundant of operator parsers for MindSpore, TensorFlow Lite, Caffe, ONNX,
+    and supports common neural networks in CV and NLP, 208+ CPU operators, and 60+ GPU operators.
+
+- High performance
+
+    Many optimization methods, including graph optimizations, post training quantization,
+    are applied to model in offline converter, and generated target model is more compact.
+    Graph optimizations, such as operator fusion and constant folding, make model more compact.
+    Post training quantization transfers fp32 model into fix-point int8 model.
+    It brings nearly 4x smaller model size, low latency and low consumption for inference process.
+    MS Lite also applies a variety of optimization schemes to NN operations, including using Winograd algorithm in convolution and deconvolution, Strassen algorithm in matrix multiplication.
+
+Operations support fp64, fp32, fp16 and int8, and are highly optimized with acceleration by neon instructions, hand-written assemble, multi-thread, memory reuse, heterogeneous computing, etc.
+
+- Versatility
+
+    Supports Harmony, iOS and Android os, supports smart-phones, watches, headsets, and various IoT devices.
+
+- Light weight
+
+    MS Lite is highly Optimized under GHLO and GLLO. It has small foot-print,
+    MS Lite runtime is about 800 kB, and MS Micro is less than 200 KB.
+    It is flexible and can easily deploy to mobile and a variety of embedded devices.
+### User Stories
+
+#### Generate a compact target model and low latency and low consumption runtime
+
+Since devices has limited resource with few ROM, RAM, and power, how to deploy AI model to
+device is very challenge. MS Lite aims to solve the challenge for users, and provides user-friendly, flexible tool to help users to make their own models more slim and more efficiency.
+
+## Design Details
+
+MS Lite consists of converter and runtime.
+The converter is an offline tool has three parts, frontend, IR, and backend.
+Runtime deploys to device and executes online.
+
+- **Frontend.** Frontend aims to parse model from MindSpore, TensorFlow Lite, Caffe and ONNX in protobuf.
+- **IR.** IR is to define ANF, including tensor, operations, and graph.
+- **Backend.** Backend is an optimizer based ANF graph, including GHLO, GLLO, and quantization. `GHLO` is short for "graph high level optimization", common optimization methods, such as operators fusion, operator substitution, and constant folding, are included. `GLLO` is short for "graph low level optimization", low level optimization methods are related to hardware, such as layout adjustment, mixed-precision, etc.
+- **Runtime.** Runtime has Lite RT and Lite Micro two modes.
+
+  <img src="./ms-lite-arch.jpg" style="zoom:80%" div align=center/>
+
+### Test Plan
+
+MS Lite employed pytests and nosetest to launch the testing process, and there are two types of testing strategies in MS Lite:
+
+- **Unit Test.** Every operation, optimization or pass in MS has its own unitest.
+
+- **System test**. The ms Lite module has its own component testing.
+Basically we classify the testing into compilation verification, function verification and performance testing.
+
+## Implementation History
+- Support high and low level graph optimization.
+- Support post training quantization.
+- Support Arm CPU and Mali GPU.
+- Support fp64, fp32, fp16, int8 operations.
+
+## Drawbacks
+- MS Lite does not support on-device training yet, it is coming soon...
+
+## Alternatives
+- MNN[1], TF Lite[2] and TNN[3] are outstanding on-device AI frameworks.
+MS Lite is for on-device AI, and MS cloud is for on-cloud AI, both of them are in scope of Huawei's MindSpore AI framework.
+They share same IR, and optimization passes. MS Lite is more flexible.
+
+## References
+- [1] https://github.com/alibaba/MNN
+- [2] https://www.tensorflow.org/lite
+- [3] https://github.com/Tencent/TNN