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vector_search.h 1.7 kB

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  1. /**
  2. * Copyright 2021 Huawei Technologies Co., Ltd
  3. *
  4. * Licensed under the Apache License, Version 2.0 (the "License");
  5. * you may not use this file except in compliance with the License.
  6. * You may obtain a copy of the License at
  7. *
  8. * http://www.apache.org/licenses/LICENSE-2.0
  9. *
  10. * Unless required by applicable law or agreed to in writing, software
  11. * distributed under the License is distributed on an "AS IS" BASIS,
  12. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13. * See the License for the specific language governing permissions and
  14. * limitations under the License.
  15. */
  16. /*!
  17. * \file vector_search.h
  18. * \brief
  19. */
  20. #ifndef OPS_BUILT_IN_OP_PROTO_INC_VECTOR_SEARCH_H_
  21. #define OPS_BUILT_IN_OP_PROTO_INC_VECTOR_SEARCH_H_
  22. #include "graph/operator_reg.h"
  23. namespace ge {
  24. /**
  25. * @brief Generate ADC(asymmetric distance computation) table. \n
  26. *
  27. * @par Inputs:
  28. * Four inputs, including:
  29. * @li query: A Tensor. Must be one of the following types: float16, float32.
  30. * @li code_book: A Tensor. Must be one of the following types: float16, float32.
  31. * @li centroids: A Tensor. Must be one of the following types: float16, float32.
  32. * @li bucket_list: A Tensor. Must be one of the following types: int32, int64.
  33. *
  34. * @par Outputs:
  35. * @li adc_tables: A Tensor. Must be one of the following types: float16, float32.
  36. */
  37. REG_OP(GenADC)
  38. .INPUT(query, TensorType({DT_FLOAT16, DT_FLOAT}))
  39. .INPUT(code_book, TensorType({DT_FLOAT16, DT_FLOAT}))
  40. .INPUT(centroids, TensorType({DT_FLOAT16, DT_FLOAT}))
  41. .INPUT(bucket_list, TensorType({DT_INT32, DT_INT64}))
  42. .OUTPUT(adc_tables, TensorType({DT_FLOAT16, DT_FLOAT}))
  43. .OP_END_FACTORY_REG(GenADC)
  44. } // namespace ge
  45. #endif // OPS_BUILT_IN_OP_PROTO_INC_VECTOR_SEARCH_H_

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