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npu_memory_allocator.cc 5.7 kB

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  1. /**
  2. * Copyright 2019-2020 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 "npu_memory_allocator.h"
  17. #include <mutex>
  18. #include "framework/common/debug/log.h"
  19. #include "graph/manager/graph_caching_allocator.h"
  20. #include "graph/manager/graph_mem_allocator.h"
  21. #include "graph/manager/rdma_pool_allocator.h"
  22. #include "graph/manager/host_mem_allocator.h"
  23. namespace ge {
  24. namespace hybrid {
  25. const size_t kPaddingUnit = 2;
  26. size_t kMaxHbmMemorySize = 1024UL * 1024UL * 1024UL * 1024UL; // 1024G
  27. std::map<uint32_t, std::unique_ptr<NpuMemoryAllocator>> NpuMemoryAllocator::allocators_;
  28. std::mutex NpuMemoryAllocator::mu_;
  29. AllocationAttr::AllocationAttr(int padding, void *try_reuse_addr, MemStorageType mem_type)
  30. : padding_(padding), try_reuse_addr_(try_reuse_addr), mem_type_(mem_type) {}
  31. AllocationAttr::AllocationAttr(int padding) : AllocationAttr(padding, nullptr) {}
  32. AllocationAttr::AllocationAttr(void *try_reuse_addr) : AllocationAttr(0, try_reuse_addr) {}
  33. NpuMemoryAllocator *NpuMemoryAllocator::GetAllocator() {
  34. int32_t device_id = 0;
  35. auto rt_result = rtGetDevice(&device_id);
  36. if (rt_result != RT_ERROR_NONE) {
  37. GELOGE(RT_FAILED, "[Get][Device] Failed, result:%d.", rt_result);
  38. REPORT_INNER_ERROR("E19999", "rtGetDevice failed when NpuMemoryAllocator %s, result:%d.",
  39. __FUNCTION__, rt_result);
  40. return nullptr;
  41. }
  42. GELOGD("Got device id = %d from context", device_id);
  43. return GetAllocator(static_cast<uint32_t>(device_id));
  44. }
  45. NpuMemoryAllocator::NpuMemoryAllocator(uint32_t device_id) : device_id_(device_id) {}
  46. void *NpuMemoryAllocator::Allocate(std::size_t size, AllocationAttr *attr) {
  47. size_t allocate_size = size;
  48. MemStorageType mem_type = HBM;
  49. if (attr != nullptr) {
  50. mem_type = attr->mem_type_;
  51. }
  52. if (allocate_size == 0) {
  53. GELOGE(MEMALLOC_FAILED, "[Check][Param:size_t]Memory size is 0, device_id = %u, size = %zu.",
  54. device_id_, allocate_size);
  55. REPORT_INNER_ERROR("E19999", "Memory size is 0, device_id = %u, size = %zu when %s.",
  56. device_id_, allocate_size, __FUNCTION__);
  57. return nullptr;
  58. }
  59. void *buffer = nullptr;
  60. if (mem_type == RDMA_HBM) {
  61. buffer = MemManager::Instance().RdmaPoolInstance(RT_MEMORY_HBM).Malloc(allocate_size, device_id_);
  62. } else if (mem_type == HOST_DDR) {
  63. buffer = MemManager::Instance().HostMemInstance(RT_MEMORY_HBM).Malloc(allocate_size);
  64. } else {
  65. if (allocate_size > kMaxHbmMemorySize) {
  66. GELOGE(PARAM_INVALID, "[Check][Param:size_t]Invalid HBM memory size: %zu bigger than limit:%lu, check invalid.",
  67. allocate_size, kMaxHbmMemorySize);
  68. REPORT_CALL_ERROR("E19999", "Invalid HBM memory size: %zu bigger than limit:%lu, check invalid when %s.",
  69. allocate_size, kMaxHbmMemorySize, __FUNCTION__);
  70. return nullptr;
  71. }
  72. void *try_reuse_addr = nullptr;
  73. int padding = kDefaultPadding;
  74. if (attr != nullptr) {
  75. try_reuse_addr = attr->try_reuse_addr_;
  76. if (attr->padding_ > 0) {
  77. padding = attr->padding_;
  78. }
  79. }
  80. // padding up to multiple of padding, and add extra padding
  81. allocate_size = (size + kPaddingUnit * padding - 1) / padding * padding;
  82. GELOGD("Padding size %ld by %d. final size = %zu.", size, padding, allocate_size);
  83. buffer = MemManager::Instance()
  84. .CachingInstance(RT_MEMORY_HBM)
  85. .Malloc(allocate_size, reinterpret_cast<uint8_t *>(try_reuse_addr), device_id_);
  86. }
  87. if (buffer == nullptr) {
  88. GELOGE(MEMALLOC_FAILED, "[Malloc][Memory] Failed, device_id = %u, size = %zu.",
  89. device_id_, allocate_size);
  90. REPORT_CALL_ERROR("E19999", "malloc memory failed, device_id = %u, size = %zu when %s.",
  91. device_id_, allocate_size, __FUNCTION__);
  92. return nullptr;
  93. }
  94. GELOGI("Allocating buffer of size %zu successfully. device_id = %u, address = %p", allocate_size, device_id_, buffer);
  95. return buffer;
  96. }
  97. void NpuMemoryAllocator::Deallocate(void *data, MemStorageType mem_type) {
  98. GELOGI("To deallocating buffer, addr = %p", data);
  99. if (data != nullptr) {
  100. GELOGI("Deallocating buffer successfully. addr = %p", data);
  101. if (mem_type == RDMA_HBM) {
  102. MemManager::Instance().RdmaPoolInstance(RT_MEMORY_HBM).Free(reinterpret_cast<uint8_t *>(data), device_id_);
  103. } else if (mem_type == HOST_DDR) {
  104. MemManager::Instance().HostMemInstance(RT_MEMORY_HBM).Free(data);
  105. } else {
  106. MemManager::Instance().CachingInstance(RT_MEMORY_HBM).Free(reinterpret_cast<uint8_t *>(data), device_id_);
  107. }
  108. }
  109. }
  110. NpuMemoryAllocator *NpuMemoryAllocator::GetAllocator(uint32_t device_id) {
  111. std::lock_guard<std::mutex> lk(mu_);
  112. auto it = allocators_.find(device_id);
  113. if (it == allocators_.end()) {
  114. auto allocator = std::unique_ptr<NpuMemoryAllocator>(new (std::nothrow) NpuMemoryAllocator(device_id));
  115. if (allocator == nullptr) {
  116. return nullptr;
  117. }
  118. allocators_.emplace(device_id, std::move(allocator));
  119. }
  120. return allocators_[device_id].get();
  121. }
  122. void NpuMemoryAllocator::DestroyAllocator() {
  123. std::lock_guard<std::mutex> lk(mu_);
  124. int device_id = 0;
  125. allocators_.erase(device_id);
  126. }
  127. } // namespace hybrid
  128. } // 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