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prof_acl_api.h 5.8 kB

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  1. /*
  2. * Copyright (c) Huawei Technologies Co., Ltd. 2019-2021. All rights reserved.
  3. * Description: handle perf data
  4. * Author: xp
  5. * Create: 2019-10-13
  6. */
  7. #ifndef MSPROFILER_API_PROF_ACL_API_H_
  8. #define MSPROFILER_API_PROF_ACL_API_H_
  9. #include <cstdint>
  10. #include <cstddef>
  11. // DataTypeConfig
  12. #define PROF_ACL_API 0x00000001ULL
  13. #define PROF_TASK_TIME 0x00000002ULL
  14. #define PROF_AICORE_METRICS 0x00000004ULL
  15. #define PROF_AICPU_TRACE 0x00000008ULL
  16. #define PROF_L2CACHE 0x00000010ULL
  17. #define PROF_HCCL_TRACE 0x00000020ULL
  18. #define PROF_TRAINING_TRACE 0x00000040ULL
  19. #define PROF_MSPROFTX 0x00000080ULL
  20. #define PROF_RUNTIME_API 0x00000100ULL
  21. #define PROF_TASK_FRAMEWORK 0x00000200ULL
  22. #define PROF_TASK_TSFW 0x00000400ULL
  23. // system profilinig switch
  24. #define PROF_CPU 0x00010000ULL
  25. #define PROF_HARDWARE_MEMORY 0x00020000ULL
  26. #define PROF_IO 0x00040000ULL
  27. #define PROF_INTER_CONNECTION 0x00080000ULL
  28. #define PROF_DVPP 0x00100000ULL
  29. #define PROF_SYS_AICORE_SAMPLE 0x00200000ULL
  30. #define PROF_AIVECTORCORE_SAMPLE 0x00400000ULL
  31. #define PROF_MODEL_EXECUTE 0x0000001000000ULL
  32. #define PROF_RUNTIME_TRACE 0x0000004000000ULL
  33. #define PROF_SCHEDULE_TIMELINE 0x0000008000000ULL
  34. #define PROF_SCHEDULE_TRACE 0x0000010000000ULL
  35. #define PROF_AIVECTORCORE_METRICS 0x0000020000000ULL
  36. #define PROF_SUBTASK_TIME 0x0000040000000ULL
  37. #define PROF_OP_DETAIL 0x0000080000000ULL
  38. constexpr uint64_t PROF_AICPU_MODEL = 0x4000000000000000ULL;
  39. #define PROF_MODEL_LOAD 0x8000000000000000ULL
  40. #define PROF_TASK_TRACE (PROF_MODEL_EXECUTE | PROF_RUNTIME_TRACE | PROF_TRAINING_TRACE | \
  41. PROF_HCCL_TRACE | PROF_TASK_TIME)
  42. // DataTypeConfig MASK
  43. #define PROF_ACL_API_MASK 0x00000001ULL
  44. #define PROF_TASK_TIME_MASK 0x00000002ULL
  45. #define PROF_AICORE_METRICS_MASK 0x00000004ULL
  46. #define PROF_AICPU_TRACE_MASK 0x00000008ULL
  47. #define PROF_L2CACHE_MASK 0x00000010ULL
  48. #define PROF_HCCL_TRACE_MASK 0x00000020ULL
  49. #define PROF_TRAINING_TRACE_MASK 0x00000040ULL
  50. #define PROF_MSPROFTX_MASK 0x00000080ULL
  51. #define PROF_RUNTIME_API_MASK 0x00000100ULL
  52. #define PROF_TASK_FRAMEWORK_MASK 0x00000200ULL
  53. #define PROF_TASK_TSFW_MASK 0x00000400ULL
  54. // system profilinig mask
  55. #define PROF_CPU_MASK 0x00010000ULL
  56. #define PROF_HARDWARE_MEMORY_MASK 0x00020000ULL
  57. #define PROF_IO_MASK 0x00040000ULL
  58. #define PROF_INTER_CONNECTION_MASK 0x00080000ULL
  59. #define PROF_DVPP_MASK 0x00100000ULL
  60. #define PROF_SYS_AICORE_SAMPLE_MASK 0x00200000ULL
  61. #define PROF_AIVECTORCORE_SAMPLE_MASK 0x00400000ULL
  62. #define PROF_MODEL_EXECUTE_MASK 0x0000001000000ULL
  63. #define PROF_RUNTIME_TRACE_MASK 0x0000004000000ULL
  64. #define PROF_SCHEDULE_TIMELINE_MASK 0x0000008000000ULL
  65. #define PROF_SCHEDULE_TRACE_MASK 0x0000010000000ULL
  66. #define PROF_AIVECTORCORE_METRICS_MASK 0x0000020000000ULL
  67. #define PROF_SUBTASK_TIME_MASK 0x0000040000000ULL
  68. #define PROF_OP_DETAIL_MASK 0x0000080000000ULL
  69. constexpr uint64_t PROF_AICPU_MODEL_MASK = 0x4000000000000000ULL;
  70. #define PROF_MODEL_LOAD_MASK 0x8000000000000000ULL
  71. #if (defined(_WIN32) || defined(_WIN64) || defined(_MSC_VER))
  72. #define MSVP_PROF_API __declspec(dllexport)
  73. #else
  74. #define MSVP_PROF_API __attribute__((visibility("default")))
  75. #endif
  76. namespace Msprofiler {
  77. namespace Api {
  78. /**
  79. * @name ProfGetOpExecutionTime
  80. * @brief get op execution time of specific part of data
  81. * @param data [IN] data read from pipe
  82. * @param len [IN] data length
  83. * @param index [IN] index of part(op)
  84. * @return op execution time (us)
  85. */
  86. MSVP_PROF_API uint64_t ProfGetOpExecutionTime(const void *data, uint32_t len, uint32_t index);
  87. }
  88. }
  89. #ifdef __cplusplus
  90. extern "C" {
  91. #endif
  92. MSVP_PROF_API uint64_t ProfGetOpExecutionTime(const void *data, uint32_t len, uint32_t index);
  93. using Status = int32_t;
  94. typedef struct aclprofSubscribeConfig aclprofSubscribeConfig1;
  95. ///
  96. /// @ingroup AscendCL
  97. /// @brief subscribe profiling data of graph
  98. /// @param [in] graphId: the graph id subscribed
  99. /// @param [in] profSubscribeConfig: pointer to config of model subscribe
  100. /// @return Status result of function
  101. ///
  102. MSVP_PROF_API Status aclgrphProfGraphSubscribe(const uint32_t graphId,
  103. const aclprofSubscribeConfig1 *profSubscribeConfig);
  104. ///
  105. /// @ingroup AscendCL
  106. /// @brief unsubscribe profiling data of graph
  107. /// @param [in] graphId: the graph id subscribed
  108. /// @return Status result of function
  109. ///
  110. MSVP_PROF_API Status aclgrphProfGraphUnSubscribe(const uint32_t graphId);
  111. /**
  112. * @ingroup AscendCL
  113. * @brief get graph id from subscription data
  114. *
  115. * @param opInfo [IN] pointer to subscription data
  116. * @param opInfoLen [IN] memory size of subscription data
  117. *
  118. * @retval graph id of subscription data
  119. * @retval 0 for failed
  120. */
  121. MSVP_PROF_API size_t aclprofGetGraphId(const void *opInfo, size_t opInfoLen, uint32_t index);
  122. /**
  123. * @ingroup AscendCL
  124. * @brief set stamp pay load
  125. *
  126. *
  127. * @retval void
  128. */
  129. MSVP_PROF_API int aclprofSetStampPayload(void *stamp, const int32_t type, void *value);
  130. /**
  131. * @ingroup AscendCL
  132. * @brief set category and name
  133. *
  134. *
  135. * @retval void
  136. */
  137. MSVP_PROF_API int aclprofSetCategoryName(uint32_t category, const char *categoryName);
  138. /**
  139. * @ingroup AscendCL
  140. * @brief set category to stamp
  141. *
  142. *
  143. * @retval void
  144. */
  145. MSVP_PROF_API int aclprofSetStampCategory(void *stamp, uint32_t category);
  146. #ifdef __cplusplus
  147. }
  148. #endif
  149. #endif // MSPROFILER_API_PROF_ACL_API_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