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RELEASE.md 3.9 kB

5 years ago
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  1. # Release 0.3.0-alpha
  2. ## Major Features and Improvements
  3. - It supports dynamic batches and shapes with certain fixed levels.([!22](https://gitee.com/mindspore/graphengine/pulls/22))
  4. - Scope fusion interfaces are opened allowing user defined scope fusion rules.([!24](https://gitee.com/mindspore/graphengine/pulls/22))
  5. - Enhance the maintenance and measurement capability.([!28](https://gitee.com/mindspore/graphengine/pulls/24))
  6. - A package of compiled libraries is generated after compilation to facilitate code deployment.([!21](https://gitee.com/mindspore/graphengine/pulls/21))
  7. ## Bugfixes
  8. - Fix the bug that the interface of GE IR construction operator does not support dynamic input in the middle of the ordinary input port.([!24](https://gitee.com/mindspore/graphengine/pulls/24))
  9. - Fix checkpoint subgraph validation and data callback process to resolve the problem that checkpoint could not be generated in some scenarios.([!28](https://gitee.com/mindspore/graphengine/pulls/28))
  10. - When MindSpore is compiled in a directory involving symbolic links, GE log records real path of which code was built when executing testcases using installed whl package.([!16](https://gitee.com/mindspore/graphengine/pulls/16), [!489](https://gitee.com/mindspore/mindspore/pulls/489))
  11. - Find third-party software in specified directories only.([!18](https://gitee.com/mindspore/graphengine/pulls/18))
  12. ## Thanks to our Contributors
  13. Thanks goes to these wonderful people:
  14. wangcong,weiyang,yanghaorang,xutianchun,shibeiji
  15. Contributions of any kind are welcome!
  16. # Release 0.2.0-alpha
  17. ## Major Features and Improvements
  18. - Provides a common graph-level option, and multiple requirements can also share this mechanism in the future.
  19. - Improve graph compilation performance.
  20. - Optimize memory allocation.
  21. - Optimize serveral operators e.g., Slice, StridedSlice, ScatterMax etc.
  22. ## Bugfixes
  23. - Delete redudant codes.([#I1EU2Z](https://gitee.com/mindspore/graphengine/issues/I1EU2Z))
  24. - Fix HCCL initilization bugs under train and eval scenarios.([#I1DIBJ](https://gitee.com/mindspore/graphengine/issues/I1DIBJ))
  25. - Optimize compilation and linking process, enhancing efficiency and performance of concurrent compilation of GraphEngine and MindSpore. ([#I1DFIY](https://gitee.com/mindspore/mindspore/issues/I1DFIY))
  26. - Fix the bug that GE checkpoint cannot save variable names correctly.([#I1DIBJ](https://gitee.com/mindspore/graphengine/issues/I1DIBJ))
  27. - Save dump files on every iteration instead of every execution.([#I1DIBJ](https://gitee.com/mindspore/graphengine/issues/I1DIBJ))
  28. ## Thanks to our Contributors
  29. Thanks goes to these wonderful people: Wang Cong, Tianchun Xu, Haoran Yang.
  30. Contributions of any kind are welcome!
  31. # Release 0.1.0-alpha
  32. This is the initial release of GraphEngine(GE) which was designed by the researchers and engineers in Huawei Technologies Co.,Ltd. GE is implemented via C++ and acts as a powerful backing force for MindSpore. GE is a linked up module between MindSpore front end and Ascend Chips.
  33. ## Main features
  34. - GE API
  35. - GE provides an unified interface with the front end including graph management interfaces i.e., graph loading and graph execution, and GE core initiallization and finalization interfaces.
  36. - Graph Processing
  37. - Six parts of graph processing operations are executed within GE, i.e. graph preparation, graph partition, graph optimization, graph compilation, graph loading and graph execution.
  38. - GE highly optimizes the graph defined by the front end to a form which can be effectively executed on Ascend Chips.
  39. - GE supports several optimizations to reduce computation costs, e.g. operator fusion, parallel operation, data format transition.
  40. - Debugging
  41. - GE supports data dump and graph dump for debugging.
  42. - GE provides profiling tools to thoroughly analyze the speed performances.

图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示