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memory_api.cc 4.6 kB

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  1. /**
  2. * Copyright 2020 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. #include "framework/memory/memory_api.h"
  17. #include <memory>
  18. #include "common/ge/plugin_manager.h"
  19. #include "graph/manager/graph_mem_manager.h"
  20. #include "graph/manager/host_mem_manager.h"
  21. #include "graph/manager/rdma_pool_allocator.h"
  22. #include "graph/utils/type_utils.h"
  23. #include "hccl/base.h"
  24. #include "hccl/hccl_types.h"
  25. namespace ge {
  26. Status InitRdmaPool(size_t size, rtMemType_t mem_type) {
  27. GELOGD("InitRdmaPool in");
  28. return MemManager::Instance().RdmaPoolInstance(mem_type).InitMemory(size);
  29. }
  30. Status RdmaRemoteRegister(const std::vector<HostVarInfo> &var_info, rtMemType_t mem_type) {
  31. GELOGD("Start to register rdma memory with host var size %zu", var_info.size());
  32. uint64_t device_base = 0;
  33. uint64_t device_size = 0;
  34. GE_CHK_STATUS_RET(MemManager::Instance().RdmaPoolInstance(mem_type).GetBaseAddr(device_base, device_size));
  35. auto table_len = var_info.size() + 1;
  36. std::unique_ptr<MemRegisterAddr[]> reg_addrs(new (std::nothrow) MemRegisterAddr[table_len]);
  37. GE_CHECK_NOTNULL(reg_addrs);
  38. for (size_t i = 0; i < var_info.size(); ++i) {
  39. reg_addrs[i] = {var_info[i].base_addr, var_info[i].var_size};
  40. }
  41. reg_addrs[table_len - 1] = {device_base, device_size};
  42. std::string file_name = "libhccl.so";
  43. std::string path = PluginManager::GetPath();
  44. path.append(file_name);
  45. string canonical_path = RealPath(path.c_str());
  46. if (canonical_path.empty()) {
  47. REPORT_INNER_ERROR("E19999", "canonical_path:%s is empty, check invalid",
  48. canonical_path.c_str());
  49. GELOGE(FAILED, "Failed to get realpath of %s", path.c_str());
  50. return FAILED;
  51. }
  52. GELOGI("FileName:%s, Path:%s.", file_name.c_str(), canonical_path.c_str());
  53. auto handle = dlopen(canonical_path.c_str(), RTLD_NOW | RTLD_GLOBAL);
  54. GE_CHECK_NOTNULL(handle);
  55. GE_MAKE_GUARD(not_used_var, [&] {
  56. if (dlclose(handle) != 0) {
  57. GELOGW("Failed to close handle %s", dlerror());
  58. }
  59. });
  60. auto hcom_remote_mem_register =
  61. (HcclResult(*)(const MemRegisterAddr *, uint32_t))dlsym(handle, "HcomRegRemoteAccessMem");
  62. if (hcom_remote_mem_register == nullptr) {
  63. REPORT_CALL_ERROR("E19999", "Symbol HcomRegRemoteAccessMem can't find in %s, check invalid",
  64. canonical_path.c_str());
  65. GELOGE(FAILED, "Failed to invoke hcom_remote_mem_register function.");
  66. return FAILED;
  67. }
  68. HcclResult hccl_ret = hcom_remote_mem_register(reg_addrs.get(), table_len);
  69. if (hccl_ret != HCCL_SUCCESS) {
  70. REPORT_CALL_ERROR("E19999", "Call hcom_remote_mem_register failed, ret:%d,",
  71. hccl_ret);
  72. GELOGE(HCCL_E_INTERNAL, "Rdma mem register failed, ret: 0x%X", hccl_ret);
  73. return HCCL_E_INTERNAL;
  74. }
  75. return SUCCESS;
  76. }
  77. Status MallocSharedMemory(const TensorInfo &tensor_info, uint64_t &dev_addr, uint64_t &memory_size) {
  78. GELOGD("MallocSharedMemory in");
  79. uint32_t type_size = 0;
  80. bool result = TypeUtils::GetDataTypeLength(tensor_info.data_type, type_size);
  81. if (!result) {
  82. GELOGE(GRAPH_FAILED, "GetDataTypeLength failed, data_type=(%s).",
  83. TypeUtils::DataTypeToSerialString(tensor_info.data_type).c_str());
  84. return GRAPH_FAILED;
  85. }
  86. memory_size = type_size;
  87. for (auto dim : tensor_info.dims) {
  88. if (dim <= 0) {
  89. GELOGE(GRAPH_FAILED, "Tensor dims should be positive");
  90. return GRAPH_FAILED;
  91. }
  92. memory_size *= dim;
  93. }
  94. SharedMemInfo mem_info(tensor_info.var_name, memory_size);
  95. Status ret = HostMemManager::Instance().MallocSharedMemory(mem_info);
  96. if (ret != SUCCESS) {
  97. GELOGE(GRAPH_FAILED, "MallocSharedMemory failed op name [%s]", tensor_info.var_name.c_str());
  98. return GRAPH_FAILED;
  99. }
  100. dev_addr = reinterpret_cast<uint64_t>(reinterpret_cast<uintptr_t>(mem_info.device_address));
  101. GELOGD("MallocSharedMemory Succeeded");
  102. return SUCCESS;
  103. }
  104. Status GetVarBaseAddrAndSize(const string &var_name, uint64_t &base_addr, uint64_t &var_size) {
  105. GELOGD("GetVarBaseAddrAndSize in");
  106. return HostMemManager::Instance().QueryVarMemInfo(var_name, base_addr, var_size);
  107. }
  108. } // namespace ge

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