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昇腾小AI

aclnnBatchNormElemtBackward

支持的产品型号

  • Atlas 训练系列产品。
  • Atlas A2训练系列产品/Atlas 800I A2推理产品。

接口原型

每个算子分为两段式接口,必须先调用“aclnnBatchNormElemtBackwardGetWorkspaceSize”接口获取入参并根据流程计算所需workspace大小,再调用“aclnnBatchNormElemtBackward”接口执行计算。

  • aclnnStatus aclnnBatchNormElemtBackwardGetWorkspaceSize(const aclTensor* gradOut, const aclTensor* input, const aclTensor* mean, const aclTensor* invstd, const aclTensor* weight, const aclTensor* sumDy, const aclTensor* sumDyXmu, aclTensor* counter, aclTensor* gradInput, uint64_t* workspaceSize, aclOpExecutor** executor)
  • aclnnStatus aclnnBatchNormElemtBackward(void* workspace, uint64_t workspaceSize, aclOpExecutor* executor, const aclrtStream stream)

功能描述

lx^i=lyiγ\frac{\partial l}{\partial \hat{x}_i} = \frac{\partial l}{\partial y_i} \cdot γ lσB2=i=0mlx^i(xiμB)12(σB2+ε)3/2\frac{\partial l}{\partial σ^2_B} = \sum^m_{i=0}\frac{\partial l}{\partial \hat{x}_i} \cdot (x_i-μ_B) \cdot \frac{-1}{2}(σ^2_B + ε)^{-3/2} lμB=(i=0mlx^i1σB2+ε)+lσB2i=0m2(xiμB)m\frac{\partial l}{\partial μ_B} = (\sum^m_{i=0}\frac{\partial l}{\partial \hat{x}_i} \cdot \frac{-1}{\sqrt{σ^2_B + ε}}) + \frac{\partial l}{\partial σ^2_B} \cdot \frac{\sum^m_{i=0}-2(x_i-μ_B)}{m} lxi=lx^i1σB2+ε)+lσB22(xiμB)m+lμB1m\frac{\partial l}{\partial x_i} = \frac{\partial l}{\partial \hat{x}_i} \cdot \frac{1}{\sqrt{σ^2_B + ε}}) + \frac{\partial l}{\partial σ^2_B} \cdot \frac{2(x_i-μ_B)}{m} + \frac{\partial l}{\partial μ_B} \cdot \frac{1}{m} lγ=i=0mlyix^\frac{\partial l}{\partial γ} = \sum^m_{i=0} \frac{\partial l}{\partial y_i} \cdot \hat{x} lβ=i=0mlyi\frac{\partial l}{\partial β} = \sum^m_{i=0} \frac{\partial l}{\partial y_i}

aclnnBatchNormElemtBackwardGetWorkspaceSize

  • 参数说明:

    • gradOut(aclTensor*, 计算输入): 正向输出的微分,Device侧的aclTensor,数据类型仅支持FLOAT、FLOAT16,shape支持2-8维,shape需要与input一致,支持非连续的Tensor数据格式支持ND,其中第2维固定为channel轴。
    • input(aclTensor*, 计算输入): Device侧的aclTensor,数据类型仅支持FLOAT、FLOAT16,shape支持2-8维,支持非连续的Tensor数据格式支持ND,其中第2维固定为channel轴。
    • mean(aclTensor*, 计算输入): 均值,Device侧的aclTensor,数据类型支持FLOAT、FLOAT16,shape支持1维,size需要与input的channel轴的size一致,支持非连续的Tensor数据格式支持ND。
    • invstd(aclTensor*, 计算输入): 标准差的倒数,Device侧的aclTensor,数据类型支持FLOAT、FLOAT16,shape支持1维,size需要与input的channel轴的size一致,支持非连续的Tensor数据格式支持ND。
    • weight(aclTensor*, 计算输入): 权重,Device侧的aclTensor,数据类型支持FLOAT、FLOAT16,shape支持1维,size需要与input的channel轴的size一致,支持非连续的Tensor数据格式支持ND。
    • sumDy(aclTensor*, 计算输入): 输出梯度的样本均值和的平均值,Device侧的aclTensor,数据类型支持FLOAT、FLOAT16,shape支持1维,size需要与input的channel轴的size一致,支持非连续的Tensor数据格式支持ND。
    • sumDyXmu(aclTensor*, 计算输入): 样本均值和与输入梯度乘积的平均值,Device侧的aclTensor,数据类型支持FLOAT、FLOAT16,shape支持1维,size需要与input的channel轴的size一致,支持非连续的Tensor数据格式支持ND。
    • counter(aclTensor*, 计算输入): 输入数据的数量大小,Device侧的aclTensor,数据类型支持INT32、FLOAT16、FLOAT,支持非连续的Tensor数据格式支持ND。
    • gradInput(aclTensor*, 计算输出): 输出的结果,Device侧的aclTensor,数据类型支持FLOAT、FLOAT16,shape支持2-8维,shape需要与input一致,支持非连续的Tensor数据格式支持ND。
    • workspaceSize(uint64_t*, 出参): 返回需要在Device侧申请的workspace大小。
    • executor(aclOpExecutor**, 出参): 返回op执行器,包含了算子计算流程。
  • 返回值:

    aclnnStatus:返回状态码,具体参见aclnn返回码

    第一段接口完成入参校验,出现以下场景时报错:
    返回161001(ACLNN_ERR_PARAM_NULLPTR):1. 传入的gradOut、input、mean、invstd、sumDy、sumDyXmu、counter或gradInput是空指针。
    返回161002(ACLNN_ERR_PARAM_INVALID):1. gradOut、input、mean、invstd、sumDy、sumDyXmu、counter、gradInput的数据类型不在支持的范围之内。
                                          2. 当weight非空指针时,weight的数据类型不在支持的范围之内。
                                          3. gradOut、input或gradInput的数据格式不在支持的范围之内。
                                          4. input的维度小于2维。
                                          5. input、gradOut、gradInput或counter的维度大于8维。
                                          6. input的channel轴的size为0。
                                          7. gradOut或gradInput的shape与input不一致。
                                          8. mean、invstd、sumDy或sumDyXmu的shape与input的channel轴不一致。
                                          9. 当weight非空指针时,weight的shape与input的channel轴不一致。

aclnnBatchNormElemtBackward

  • 参数说明:

    • workspace(void*, 入参): 在Device侧申请的workspace内存地址。
    • workspaceSize(uint64_t, 入参): 在Device侧申请的workspace大小,由第一段接口aclnnBatchNormElemtBackwardGetWorkspaceSize获取。
    • executor(aclOpExecutor*, 入参): op执行器,包含了算子计算流程。
    • stream(aclrtStream, 入参): 指定执行任务的AscendCL Stream流。
  • 返回值:

    aclnnStatus:返回状态码,具体参见aclnn返回码

约束与限制

调用示例

示例代码如下,仅供参考,具体编译和执行过程请参考编译与运行样例

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_batch_norm_elemt_backward.h"

#define CHECK_RET(cond, return_expr) \
  do {                               \
    if (!(cond)) {                   \
      return_expr;                   \
    }                                \
  } while (0)

#define LOG_PRINT(message, ...)     \
  do {                              \
    printf(message, ##__VA_ARGS__); \
  } while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) {
  int64_t shapeSize = 1;
  for (auto i : shape) {
    shapeSize *= i;
  }
  return shapeSize;
}

int Init(int32_t deviceId, aclrtStream* stream) {
  // 固定写法,AscendCL初始化
  auto ret = aclInit(nullptr);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetDevice(deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
  ret = aclrtCreateStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
  return 0;
}

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) {
  auto size = GetShapeSize(shape) * sizeof(T);
  // 调用aclrtMalloc申请Device侧内存
  auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);

  // 调用aclrtMemcpy将Host侧数据拷贝到Device侧内存上
  ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);

  // 计算连续tensor的strides
  std::vector<int64_t> strides(shape.size(), 1);
  for (int64_t i = shape.size() - 2; i >= 0; i--) {
    strides[i] = shape[i + 1] * strides[i + 1];
  }

  // 调用aclCreateTensor接口创建aclTensor
  *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
                            shape.data(), shape.size(), *deviceAddr);
  return 0;
}

int main() {
  // 1. (固定写法)device/stream初始化,参考AscendCL对外接口列表
  // 根据自己的实际device填写deviceId
  int32_t deviceId = 0;
  aclrtStream stream;
  auto ret = Init(deviceId, &stream);
  // check根据自己的需要处理
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);

  // 2. 构造输入与输出,需要根据API的接口自定义构造
  std::vector<int64_t> gradOutShape = {1, 2, 4};
  std::vector<int64_t> inputShape = {1, 2, 4};
  std::vector<int64_t> meanShape = {2};
  std::vector<int64_t> invstdShape = {2};
  std::vector<int64_t> weightShape = {2};
  std::vector<int64_t> sumDyShape = {2};
  std::vector<int64_t> sumDyXmuShape = {2};
  std::vector<int64_t> counterShape = {2};
  std::vector<int64_t> gradInputShape = {1, 2, 4};
  void* gradOutDeviceAddr = nullptr;
  void* inputDeviceAddr = nullptr;
  void* meanDeviceAddr = nullptr;
  void* invstdDeviceAddr = nullptr;
  void* weightDeviceAddr = nullptr;
  void* sumDyDeviceAddr = nullptr;
  void* sumDyXmuDeviceAddr = nullptr;
  void* counterDeviceAddr = nullptr;
  void* gradInputDeviceAddr = nullptr;
  aclTensor* gradOut = nullptr;
  aclTensor* input = nullptr;
  aclTensor* mean = nullptr;
  aclTensor* invstd = nullptr;
  aclTensor* weight = nullptr;
  aclTensor* sumDy = nullptr;
  aclTensor* sumDyXmu = nullptr;
  aclTensor* counter = nullptr;
  aclTensor* gradInput = nullptr;
  std::vector<float> gradOutHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<float> inputHostData = {0, 1, 2, 3, 4, 5, 6, 7};
  std::vector<float> meanHostData = {0, 0};
  std::vector<float> invstdHostData = {1, 1};
  std::vector<float> weightHostData = {1, 1};
  std::vector<float> sumDyHostData = {0, 0};
  std::vector<float> sumDyXmuHostData = {1, 1};
  std::vector<float> counterHostData = {5, 5};
  std::vector<float> gradInputHostData(8, 0);

  // 创建gradOut aclTensor
  ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建input aclTensor
  ret = CreateAclTensor(inputHostData, inputShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建weight aclTensor
  ret = CreateAclTensor(weightHostData, weightShape, &weightDeviceAddr, aclDataType::ACL_FLOAT, &weight);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建mean aclTensor
  ret = CreateAclTensor(meanHostData, meanShape, &meanDeviceAddr, aclDataType::ACL_FLOAT, &mean);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建invstd aclTensor
  ret = CreateAclTensor(invstdHostData, invstdShape, &invstdDeviceAddr, aclDataType::ACL_FLOAT, &invstd);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建sumDy aclTensor
  ret = CreateAclTensor(sumDyHostData, sumDyShape, &sumDyDeviceAddr, aclDataType::ACL_FLOAT, &sumDy);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建sumDyXmu aclTensor
  ret = CreateAclTensor(sumDyXmuHostData, sumDyXmuShape, &sumDyXmuDeviceAddr, aclDataType::ACL_FLOAT, &sumDyXmu);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建counter aclTensor
  ret = CreateAclTensor(counterHostData, counterShape, &counterDeviceAddr, aclDataType::ACL_FLOAT, &counter);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建gradInput aclTensor
  ret = CreateAclTensor(gradInputHostData, gradInputShape, &gradInputDeviceAddr, aclDataType::ACL_FLOAT, &gradInput);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;

  // aclnnBatchNormElemtBackward接口调用示例
  // 3. 调用CANN算子库API,需要修改为具体的API名称
  // 调用aclnnBatchNormElemtBackward第一段接口
  ret = aclnnBatchNormElemtBackwardGetWorkspaceSize(gradOut, input, mean, invstd, weight, sumDy, sumDyXmu, counter, gradInput, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBatchNormElemtBackwardGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
  // 根据第一段接口计算出的workspaceSize申请device内存
  void* workspaceAddr = nullptr;
  if (workspaceSize > 0) {
    ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
  }
  // 调用aclnnBatchNormElemtBackward第二段接口
  ret = aclnnBatchNormElemtBackward(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBatchNormElemtBackward failed. ERROR: %d\n", ret); return ret);

  // 4. (固定写法)同步等待任务执行结束
  ret = aclrtSynchronizeStream(stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);

  // 5. 获取输出的值,将Device侧内存上的结果拷贝至Host侧,需要根据具体API的接口定义修改
  auto size = GetShapeSize(gradInputShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), gradInputDeviceAddr,
                    size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
  for (int64_t i = 0; i < size; i++) {
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  }

  // 6. 释放aclTensor和aclScalar,需要根据具体API的接口定义修改
  aclDestroyTensor(gradOut);
  aclDestroyTensor(input);
  aclDestroyTensor(weight);
  aclDestroyTensor(mean);
  aclDestroyTensor(invstd);
  aclDestroyTensor(sumDy);
  aclDestroyTensor(sumDyXmu);
  aclDestroyTensor(counter);
  aclDestroyTensor(gradInput);

  // 7. 释放device资源,需要根据具体API的接口定义修改
  aclrtFree(gradOutDeviceAddr);
  aclrtFree(inputDeviceAddr);
  aclrtFree(weightDeviceAddr);
  aclrtFree(meanDeviceAddr);
  aclrtFree(invstdDeviceAddr);
  aclrtFree(sumDyDeviceAddr);
  aclrtFree(sumDyXmuDeviceAddr);
  aclrtFree(counterDeviceAddr);
  aclrtFree(gradInputDeviceAddr);
  if (workspaceSize > 0) {
    aclrtFree(workspaceAddr);
  }
  aclrtDestroyStream(stream);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
}
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