Skip to main content

aclnnCtcloss

aclnnCos/aclnnInplaceCos​

接口原型​

:::note 说明

  • aclnnCos和aclnnInplaceCos实现相同的功能，其使用区别如下，请根据自身实际场景选择合适的算子。

    • aclnnCos：需新建一个输出张量对象存储计算结果。
    • aclnnInplaceCos：无需新建输出张量对象，直接在输入张量的内存中存储计算结果。

  • 每个算子分为两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。 :::

  • aclnnCos两段式接口如下：

    • **第一段接口：**aclnnStatus aclnnCosGetWorkspaceSize(const aclTensor *self, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
    • **第二段接口：**aclnnStatus aclnnAtan(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

  • aclnnInplaceCos两段式接口如下：

    • **第一段接口：**aclnnStatus aclnnInplaceCosGetWorkspaceSize(const aclTensor *selfRef, uint64_t *workspaceSize, aclOpExecutor **executor)
    • **第二段接口：**aclnnStatus aclnnInplaceCos(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

功能描述​

  • 算子功能：对输入张量每个元素取余弦并输出。

  • 计算公式：

aclnnCosGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnCosGetWorkspaceSize(const aclTensor *self, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • self：Device侧的aclTensor，数据类型支持FLOAT16、FLOAT、DOUBLE、INT32、INT64、INT16、INT8、UINT8、BOOL、COMPLEX64、COMPLEX128、BFLOAT16（仅Atlas A2训练系列产品支持）。当类型为INT8、INT16、UINT8、BOOL，INT32、INT64时，转化为FLOAT32进行运算，输出FLOAT32类型。支持非连续的Tensor，数据格式支持ND，维度不大于8，且shape需要与out一致。
    • out：Device侧的aclTensor，数据类型支持FLOAT16、FLOAT、DOUBLE、COMPLEX64、COMPLEX128、BFLOAT16（仅Atlas A2训练系列产品支持）。支持非连续的Tensor，数据格式支持ND，维度不大于8，且shape需要与self一致。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的self或out是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • 参数self、out的数据类型不在支持的范围内。
      • self和out的shape不一致。
      • self或out非连续且维数大于8。 :::

aclnnCos​

  • 接口定义：

    aclnnStatus aclnnCos(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnCosGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

aclnnInplaceCosGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnInplaceCosGetWorkspaceSize(const aclTensor *selfRef, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • selfRef：Device侧的aclTensor，数据类型支持FLOAT16、FLOAT、DOUBLE、COMPLEX64、COMPLEX128、BFLOAT16（仅Atlas A2训练系列产品支持）。支持非连续的Tensor，数据格式支持ND，维度不大于8。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的selfRef是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • selfRef的数据类型不在支持的范围内。
      • selfRef的维数大于8。 :::

aclnnInplaceCos​

  • 接口定义：

    aclnnStatus aclnnInplaceCos(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnInplaceCosGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例​

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

#define CHECK_RET(cond, return_expr) \
  do &#123;                               \
    if (!(cond)) &#123;                   \
      return_expr;                   \
    &#125;                                \
  &#125; while (0)

#define LOG_PRINT(message, ...)     \
  do &#123;                              \
    printf(message, ##__VA_ARGS__); \
  &#125; while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) &#123;
  int64_t shape_size = 1;
  for (auto i : shape) &#123;
    shape_size *= i;
  &#125;
  return shape_size;
&#125;

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) &#123;
  // 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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;
&#125;

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) &#123;
  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--) &#123;
    strides[i] = shape[i + 1] * strides[i + 1];
  &#125;

  // 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;
&#125;

int main() &#123;
  // 1. （固定写法）device/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  // check根据自己的需要处理
  CHECK_RET(ret == 0, 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> selfShape = &#123;4, 2&#125;;
  std::vector<int64_t> outShape = &#123;4, 2&#125;;
  void* selfDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> selfHostData = &#123;0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8&#125;;
  std::vector<float> outHostData = &#123;0, 0, 0, 0, 0, 0, 0, 0&#125;;
  // 创建self aclTensor
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3.调用CANN算子库API，需要修改为具体的算子接口
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnCos第一段接口
  ret = aclnnCosGetWorkspaceSize(self, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCosGetWorkspaceSize 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) &#123;
    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;);
  &#125;
  // 调用aclnnCos第二段接口
  ret = aclnnCos(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCos failed. ERROR: %d\n", ret); return ret);

  uint64_t inplaceWorkspaceSize = 0;
  aclOpExecutor* inplaceExecutor;
  // 调用aclnnInplaceCos第一段接口
  ret = aclnnInplaceCosGetWorkspaceSize(self, &inplaceWorkspaceSize, &inplaceExecutor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceCosGetWorkspaceSize 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* inplaceWorkspaceAddr  = nullptr;
  if (inplaceWorkspaceSize > 0) &#123;
    ret = aclrtMalloc(&inplaceWorkspaceAddr, inplaceWorkspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
  &#125;
  // 调用aclnnInplaceCos第二段接口
  ret = aclnnInplaceCos(inplaceWorkspaceAddr, inplaceWorkspaceSize, inplaceExecutor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnInplaceCos 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(outShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(float),
                    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++) &#123;
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  &#125;

  // 6. 释放aclTensor，需要根据具体API的接口定义修改
  aclDestroyTensor(self);
  aclDestroyTensor(out);
  return 0;
&#125;

父主题： NN类算子接口

aclnnCosh​

接口原型​

每个算子有两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。两段式接口如下：

  • **第一段接口：**aclnnStatus aclnnCoshGetWorkspaceSize(const aclTensor *self, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
  • **第二段接口：**aclnnStatus aclnnCosh(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

功能描述​

  • 算子功能：对张量self进行双曲余弦计算并输出。

  • 计算公式：

aclnnCoshGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnCoshGetWorkspaceSize(const aclTensor *self, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • self：Device侧的aclTensor，输入张量，数据类型支持FLOAT、DOUBLE、FLOAT16、INT8、INT16、INT32、INT64、UINT8、BOOL、COMPLEX64、COMPLEX128，与out的数据类型需满足数据类型推导规则，支持非连续的Tensor，数据格式支持ND，且shape需要与out一致。
    • out：Device侧的aclTensor，输出张量，数据类型支持FLOAT、DOUBLE、FLOAT16、COMPLEX64、COMPLEX128，与self的数据类型需满足数据类型推导规则，支持非连续的Tensor，数据格式支持ND，且shape需要与self一致。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的self或out是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • self、out的数据类型和数据格式不在支持的范围之内。
      • self和out的shape大小不一致。
      • self的维度超出8。 :::

aclnnCosh​

  • 接口定义：

    aclnnStatus aclnnCosh(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnCoshGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例​

#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_cosh.h"

#define CHECK_RET(cond, return_expr) \
  do &#123;                               \
    if (!(cond)) &#123;                   \
      return_expr;                   \
    &#125;                                \
  &#125; while (0)

#define LOG_PRINT(message, ...)     \
  do &#123;                              \
    printf(message, ##__VA_ARGS__); \
  &#125; while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) &#123;
  int64_t shape_size = 1;
  for (auto i : shape) &#123;
    shape_size *= i;
  &#125;
  return shape_size;
&#125;

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) &#123;
  // 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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;
&#125;

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) &#123;
  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--) &#123;
    strides[i] = shape[i + 1] * strides[i + 1];
  &#125;
  // 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;
&#125;

int main() &#123;
  // 1. （固定写法）device/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  // check根据自己的需要处理
  CHECK_RET(ret == 0, 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> selfShape = &#123;8&#125;;
  std::vector<int64_t> outShape = &#123;8&#125;;
  void* selfDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> selfHostData = &#123;0.0, 1.1, 2, 3, 4, 5, 6, 7&#125;;
  std::vector<float> outHostData = &#123;0.0, 0, 0, 0, 0, 0, 0, 0&#125;;
  // 创建self aclTensor
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3.调用CANN算子库API，需要修改为具体的算子接口
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnCosh第一段接口
  ret = aclnnCoshGetWorkspaceSize(self, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCoshGetWorkspaceSize 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) &#123;
    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;);
  &#125;
  // 调用aclnnCosh第二段接口
  ret = aclnnCosh(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCosh 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(outShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(float),
                    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++) &#123;
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  &#125;

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(self);
  aclDestroyTensor(out);
  return 0;
&#125;

父主题： NN类算子接口

aclnnCtcloss​

接口原型​

每个算子有两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。两段式接口如下：

  • **第一段接口：**aclnnStatus aclnnCtcLossGetWorkspaceSize(const aclTensor *logProbs, const aclTensor *targets, const aclIntArray *inputLengths, const aclIntArray *targetLengths, int64_t blank, bool zeroInfinity, aclTensor *negLoglikelihoodOut, aclTensor *logAlphaOut, uint64_t *workspaceSize, aclOpExecutor **executor)
  • **第二段接口：**aclnnStatus aclnnCtcLoss(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

功能描述​

  • 算子功能：计算连接时序分类损失值。

  • 计算公式：

    定义ytk表示在时刻t时真实字符为k的概率。(一般地，ytk是经过softmax之后输出矩阵中的一个元素)。将字符集L′可以构成的所有序列的集合称为L′T，将L′T中的任意一个序列称为路径，并标记为π。π的分布为公式为：

    定义多对一（many to one）映射B：L′T→L≤T，通过映射B计算得到 l∈L≤T的条件概率，等于对应于l的所有可能路径概率之和，公式如下：

    将找到使p(l|x)值最大的l的路径的任务称为解码，公式如下：

aclnnCtcLossGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnCtcLossGetWorkspaceSize(const aclTensor *logProbs, const aclTensor *targets, const aclIntArray *inputLengths, const aclIntArray *targetLengths, int64_t blank, bool zeroInfinity, aclTensor *negLoglikelihoodOut, aclTensor *logAlphaOut, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • logProbs：Device侧的aclTensor，表示输出的对数概率。数据类型支持FLOAT、DOUBLE。当shape为(T, N, C)，其中T为输入长度，N为批处理大小，C为类别数。支持非连续的Tensor，数据格式支持ND。
    • targets：Device侧的aclTensor，数据类型支持INT64、INT32、FLOAT、FLOAT16、BOOL。当shape为(N, S)，S为不小于targetLengths中的最大值。当targets是未填充的且在1维内级联的，shape为(SUM(targetLengths))。支持非连续的Tensor，数据格式支持ND。
    • inputLengths：Host侧的aclIntArray，数据类型支持UINT8、INT8、INT16、INT32、INT64，数组长度为N，数组中的每个值必须≤T。
    • targetLengths：Host侧的aclIntArray，数据类型支持UINT8、INT8、INT16、INT32、INT64，数组长度为N，当targets的shape为(N, S)时，数组中的每个值必须≤S。
    • blank：int整型，空白标识，默认为0，取值范围为[0, C），C为类别数。
    • zeroInfinity：bool类型，表示是否将无限损耗和相关梯度归零，默认值为False。
    • negLogLikelihoodOut：Device侧的aclTensor，表示输出的损失值，数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
    • logAlphaOut：Device侧的aclTensor，表示输入到目标的可能跟踪的概率，shape必须为3维。数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的logProbs、targets、inputLengths、targetLengths、negLogLikelihoodOut、logAlphaOut是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • logProbs、targets、inputLengths、targetLengths的数据类型不在支持的范围内。
      • logProbs、targets、inputLengths、targetLengths、negLogLikelihoodOut、logAlphaOut的shape不满足要求，或者inputLengths、targetLengths的ArrayList长度不满足要求。
      • blank不满足取值范围。 :::

aclnnCtcLoss​

  • 接口定义：

    aclnnStatus aclnnCtcLoss(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnCtcLossGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例​

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

#define CHECK_RET(cond, return_expr) \
  do &#123;                               \
    if (!(cond)) &#123;                   \
      return_expr;                   \
    &#125;                                \
  &#125; while (0)

#define LOG_PRINT(message, ...)     \
  do &#123;                              \
    printf(message, ##__VA_ARGS__); \
  &#125; while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) &#123;
  int64_t shape_size = 1;
  for (auto i : shape) &#123;
    shape_size *= i;
  &#125;
  return shape_size;
&#125;

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) &#123;
  // 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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;
&#125;

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) &#123;
  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--) &#123;
    strides[i] = shape[i + 1] * strides[i + 1];
  &#125;

  // 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;
&#125;

int main() &#123;
  // 1. （固定写法）device/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  // check根据自己的需要处理
  CHECK_RET(ret == 0, 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> logProbsShape = &#123;12, 4, 5&#125;;
  std::vector<int64_t> targetsShape = &#123;4, 7&#125;;
  std::vector<int64_t> negLoglikelihoodOutShape = &#123;4&#125;;
  std::vector<int64_t> logAlphaOutShape = &#123;4, 12, 16&#125;;
  void* logProbsDeviceAddr = nullptr;
  void* targetsDeviceAddr = nullptr;
  void* negLoglikelihoodOutDeviceAddr = nullptr;
  void* logAlphaOutDeviceAddr = nullptr;
  aclTensor* logProbs = nullptr;
  aclTensor* targets = nullptr;
  aclIntArray* inputLengths = nullptr;
  aclIntArray* targetLengths = nullptr;
  aclTensor* negLoglikelihoodOut = nullptr;
  aclTensor* logAlphaOut = nullptr;
  std::vector<float> logProbsHostData = &#123;
    -1.0894, -2.7162, -0.9764, -1.9126, -2.6162,
    -2.0684, -2.4871, -2.0866, -1.7205, -0.7187,
    -2.4423, -1.2017, -1.4653, -1.1821, -2.5942,
    -2.4670, -2.7257, -1.4135, -2.1042, -0.7248,

    -3.7759, -1.3742, -1.2549, -1.5807, -1.4562,
    -1.3826, -1.8995, -1.8527, -0.9493, -2.8895,
    -1.6316, -2.6603, -2.5014, -0.6992, -1.8609,
    -1.9269, -2.2350, -0.8073, -1.8906, -1.8947,

    -0.3468, -2.5855, -2.0723, -2.7147, -3.6668,
    -0.9541, -1.7258, -2.0693, -1.6378, -2.1531,
    -3.5386, -3.4830, -0.2532, -2.0557, -3.3261,
    -1.1480, -1.8080, -0.8244, -3.2414, -3.1909,

    -0.8866, -0.7540, -4.4312, -3.4634, -2.6000,
    -1.2785, -1.8347, -3.3122, -0.7620, -2.8349,
    -1.4975, -1.3865, -0.9645, -3.8171, -2.0939,
    -2.3536, -2.0773, -1.4981, -0.8372, -2.0938,

    -1.2186, -0.8285, -2.9399, -2.1159, -2.3620,
    -2.3139, -0.6503, -2.7249, -1.2340, -3.7927,
    -0.7143, -2.5084, -3.2826, -2.6651, -1.1334,
    -1.6965, -1.9728, -2.3849, -1.6052, -0.9554,

    -1.6384, -1.2596, -2.1680, -1.8476, -1.3866,
    -3.0455, -0.5737, -2.5339, -2.1118, -1.6681,
    -2.4675, -2.8842, -0.4329, -3.6266, -1.6925,
    -3.1023, -2.7696, -1.2755, -0.6470, -2.4143,

    -2.0107, -2.0912, -1.3053, -0.8557, -3.0683,
    -1.2872, -3.6523, -1.6703, -2.7596, -0.8063,
    -2.4633, -1.2959, -1.6153, -2.3072, -1.0705,
    -3.0543, -0.6473, -1.1650, -2.9025, -2.7710,

    -3.5519, -2.0400, -1.8667, -1.4289, -0.8050,
    -1.4602, -0.7452, -1.5754, -3.1624, -3.1247,
    -1.4677, -1.2725, -2.9575, -1.8883, -1.2513,
    -1.2164, -1.5894, -2.2217, -2.3714, -1.2110,

    -2.0843, -0.6515, -1.4252, -2.9402, -2.7964,
    -1.5261, -2.5471, -1.7167, -1.9846, -0.9488,
    -1.4847, -1.7093, -1.4095, -1.7293, -1.7675,
    -0.9203, -4.2299, -1.8740, -1.4076, -1.6671,

    -1.9052, -0.8330, -2.1839, -2.2459, -1.6193,
    -2.9108, -1.2114, -1.4616, -1.7297, -1.4330,
    -2.2656, -0.7878, -1.8533, -1.8711, -2.0349,
    -2.2457, -2.1395, -1.4509, -0.7538, -2.6381,

    -0.8078, -2.1054, -2.6703, -1.1108, -3.3867,
    -1.7774, -1.8426, -1.9473, -1.3293, -1.3273,
    -1.3490, -1.9842, -2.5357, -2.2161, -0.8800,
    -1.5412, -1.8003, -2.7603, -0.8606, -2.0066,

    -1.8342, -2.2741, -1.8348, -1.5833, -0.9877,
    -3.5196, -2.3361, -0.9124, -0.9307, -2.5531,
    -1.4862, -1.2153, -1.4453, -3.4462, -1.5625,
    -2.6455, -1.4153, -1.3079, -1.1568, -2.2897&#125;;
  std::vector<int64_t> targetsHostData = &#123;
    1, 2, 1, 1, 2, 4, 1,
    2, 2, 2, 2, 2, 2, 3,
    4, 2, 1, 4, 3, 1, 4,
    4, 1, 4, 2, 2, 2, 3&#125;;

  std::vector<float> negLoglikelihoodOutHostData = &#123;0, 0, 0, 0&#125;;
  std::vector<float> logAlphaOutHostData = &#123;
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0&#125;;
  // 创建logProbs aclTensor
  ret = CreateAclTensor(logProbsHostData, logProbsShape, &logProbsDeviceAddr, aclDataType::ACL_FLOAT, &logProbs);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建targets aclTensor
  ret = CreateAclTensor(targetsHostData, targetsShape, &targetsDeviceAddr, aclDataType::ACL_INT64, &targets);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  std::vector<int64_t> inputLengthsSizeData = &#123;10,10,10,10&#125;;
  inputLengths = aclCreateIntArray(inputLengthsSizeData.data(), 4);
  CHECK_RET(inputLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
  std::vector<int64_t> targetLengthsSizeData = &#123;2, 3, 1, 5&#125;;
  targetLengths = aclCreateIntArray(targetLengthsSizeData.data(), 4);
  CHECK_RET(targetLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
  // 创建negLoglikelihoodOut aclTensor
  ret = CreateAclTensor(negLoglikelihoodOutHostData, negLoglikelihoodOutShape, &negLoglikelihoodOutDeviceAddr, aclDataType::ACL_FLOAT, &negLoglikelihoodOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建logAlphaOut aclTensor
  ret = CreateAclTensor(logAlphaOutHostData, logAlphaOutShape, &logAlphaOutDeviceAddr, aclDataType::ACL_FLOAT, &logAlphaOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3.调用CANN算子库API，需要修改为具体的算子接口
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnCtcLoss第一段接口
  ret = aclnnCtcLossGetWorkspaceSize(logProbs, targets, inputLengths, targetLengths, 0, false, negLoglikelihoodOut, logAlphaOut, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossGetWorkspaceSize 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) &#123;
    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;);
  &#125;
  // 调用aclnnCtcLoss第二段接口
  ret = aclnnCtcLoss(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLoss 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negLoglikelihoodOut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(negLoglikelihoodOutShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), negLoglikelihoodOutDeviceAddr, size * sizeof(float),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++) &#123;
    LOG_PRINT("negLoglikelihoodOut result[%ld] is: %f\n", i, resultData[i]);
  &#125;

  // 6. 获取输出的logAlphaOut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 size1 = GetShapeSize(logAlphaOutShape);
  std::vector<float> resultData1(size1, 0);
  ret = aclrtMemcpy(resultData1.data(), resultData1.size() * sizeof(resultData1[0]), logAlphaOutDeviceAddr, size1 * sizeof(float),
                    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 < size1; i++) &#123;
    LOG_PRINT("logAlphaOut result[%ld] is: %f\n", i, resultData1[i]);
  &#125;

  // 7. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(logProbs);
  aclDestroyTensor(targets);
  aclDestroyIntArray(inputLengths);
  aclDestroyIntArray(targetLengths);
  aclDestroyTensor(negLoglikelihoodOut);
  aclDestroyTensor(logAlphaOut);
  return 0;
&#125;

父主题： NN类算子接口

aclnnCtcLossBackward​

接口原型​

每个算子有两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。两段式接口如下：

  • 第一段接口：aclnnStatus aclnnCtcLossBackwardGetWorkspaceSize(const aclTensor *gradOut, const aclTensor *logProbs, const aclTensor *targets, const aclIntArray *inputLengths, const aclIntArray *targetLengths, const aclTensor *negLogLikelihood, const aclTensor *logAlpha, int64_t blank, bool zeroInfinity, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)
  • 第二段接口：aclnnStatus aclnnCtcLossBackward(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

功能描述​

算子功能：损失函数aclnnCtcloss的反向传播。

aclnnCtcLossBackwardGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnCtcLossBackwardGetWorkspaceSize(const aclTensor *gradOut, const aclTensor *logProbs, const aclTensor *targets, const aclIntArray *inputLengths, const aclIntArray *targetLengths, const aclTensor *negLogLikelihood, const aclTensor *logAlpha, int64_t blank, bool zeroInfinity, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • gradOut：Device侧的aclTensor，表示梯度更新系数，必须为1维非空Tensor，数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
    • logProbs：Device侧的aclTensor，表示输出的对数概率，数据类型支持FLOAT、DOUBLE。当shape为(T, N, C)，其中T为输入长度，N为批处理大小，C为类别数。支持非连续的Tensor，数据格式支持ND。
    • targets：Device侧的aclTensor，数据类型支持INT64、INT32、FLOAT、FLOAT16、BOOL。当shape为(N, S)，S为不小于targetLengths中的最大值。当targets是未填充的且在1维内级联的，shape为(SUM(targetLengths))。支持非连续的Tensor，数据格式支持ND。
    • inputLengths：Host侧int数组，数据类型支持UINT8、INT8、INT16、INT32、INT64，数组长度为N，数组中的每个值必须≤T。
    • targetLengths：Host侧int数组，数据类型支持UINT8、INT8、INT16、INT32、INT64，数组长度为N，当targets的shape为(N, S)时，数组中的每个值必须≤S。
    • negLogLikelihood：Device侧的aclTensor，表示相对于每个输入节点可微分的损失值，必须为1维非空Tensor。数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
    • logAlpha：Device侧的aclTensor，表示输入到目标的可能跟踪的概率，必须为3维非空Tensor。数据类型支持FLOAT、DOUBLE（数据类型必须和logProbs一致），支持非连续的Tensor，数据格式支持ND。
    • blank：int整型，空白标识，默认为0，取值范围为[0, C），C为类别数。
    • zeroInfinity：bool类型，表示是否将无限损耗和相关梯度归零，默认值为False。
    • out：Device侧的aclTensor，表示CTC的损失梯度，数据类型支持FLOAT、DOUBLE（数据类型必须和gradOut一致），shape为(T, N, C)，支持非连续的Tensor，数据格式支持ND。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的gradOut、logProbs、targets、inputLengths、targetLengths、negLogLikelihood、logAlpha、out是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • gradOut、logProbs、targets、inputLengths、targetLengths、negLogLikelihood、logAlpha、out的数据类型不在支持的范围内。
      • gradOut、negLogLikelihood、logAlpha、out和logProbs的数据类型不一致。
      • gradOut、logProbs、targets、negLogLikelihood、logAlpha、out的shape不满足要求，或者inputLengths、targetLengths的ArrayList的长度不满足要求。
      • blank不满足取值范围。 :::

aclnnCtcLossBackward​

  • 接口定义：

    aclnnStatus aclnnCtcLossBackward(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, const aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnCtcLossBackwardGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例​

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

#define CHECK_RET(cond, return_expr) \
  do &#123;                               \
    if (!(cond)) &#123;                   \
      return_expr;                   \
    &#125;                                \
  &#125; while (0)

#define LOG_PRINT(message, ...)     \
  do &#123;                              \
    printf(message, ##__VA_ARGS__); \
  &#125; while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) &#123;
  int64_t shape_size = 1;
  for (auto i : shape) &#123;
    shape_size *= i;
  &#125;
  return shape_size;
&#125;

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) &#123;
  // 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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;
&#125;

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) &#123;
  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--) &#123;
    strides[i] = shape[i + 1] * strides[i + 1];
  &#125;

  // 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;
&#125;

int main() &#123;
  // 1. （固定写法）device/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  // check根据自己的需要处理
  CHECK_RET(ret == 0, 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 = &#123;4&#125;;
  // logProbsShape (T, N, C)
  std::vector<int64_t> logProbsShape = &#123;12, 4, 5&#125;;
  std::vector<int64_t> targetsShape = &#123;4, 7&#125;;
  std::vector<int64_t> negLoglikelihoodShape = &#123;4&#125;;
  // logAlphaShape (N, T, X)  X = ((max(targetLengths) * 2 + 1) + 7) / 8 * 8;
  std::vector<int64_t> logAlphaShape = &#123;4, 12, 16&#125;;
  std::vector<int64_t> outShape = &#123;12, 4, 5&#125;;
  void* gradOutDeviceAddr = nullptr;
  void* logProbsDeviceAddr = nullptr;
  void* targetsDeviceAddr = nullptr;
  void* negLoglikelihoodDeviceAddr = nullptr;
  void* logAlphaDeviceAddr = nullptr;
  void* outDeviceAddr = nullptr;
  aclTensor* gradOut = nullptr;
  aclTensor* logProbs = nullptr;
  aclTensor* targets = nullptr;
  aclIntArray* inputLengths = nullptr;
  aclIntArray* targetLengths = nullptr;
  aclTensor* negLoglikelihood = nullptr;
  aclTensor* logAlpha = nullptr;
  aclTensor* out = nullptr;
  std::vector<float> gradOutHostData = &#123;1, 1, 1, 1&#125;;
  std::vector<float> logProbsHostData = &#123;
    -1.0894, -2.7162, -0.9764, -1.9126, -2.6162,
    -2.0684, -2.4871, -2.0866, -1.7205, -0.7187,
    -2.4423, -1.2017, -1.4653, -1.1821, -2.5942,
    -2.4670, -2.7257, -1.4135, -2.1042, -0.7248,

    -3.7759, -1.3742, -1.2549, -1.5807, -1.4562,
    -1.3826, -1.8995, -1.8527, -0.9493, -2.8895,
    -1.6316, -2.6603, -2.5014, -0.6992, -1.8609,
    -1.9269, -2.2350, -0.8073, -1.8906, -1.8947,

    -0.3468, -2.5855, -2.0723, -2.7147, -3.6668,
    -0.9541, -1.7258, -2.0693, -1.6378, -2.1531,
    -3.5386, -3.4830, -0.2532, -2.0557, -3.3261,
    -1.1480, -1.8080, -0.8244, -3.2414, -3.1909,

    -0.8866, -0.7540, -4.4312, -3.4634, -2.6000,
    -1.2785, -1.8347, -3.3122, -0.7620, -2.8349,
    -1.4975, -1.3865, -0.9645, -3.8171, -2.0939,
    -2.3536, -2.0773, -1.4981, -0.8372, -2.0938,

    -1.2186, -0.8285, -2.9399, -2.1159, -2.3620,
    -2.3139, -0.6503, -2.7249, -1.2340, -3.7927,
    -0.7143, -2.5084, -3.2826, -2.6651, -1.1334,
    -1.6965, -1.9728, -2.3849, -1.6052, -0.9554,

    -1.6384, -1.2596, -2.1680, -1.8476, -1.3866,
    -3.0455, -0.5737, -2.5339, -2.1118, -1.6681,
    -2.4675, -2.8842, -0.4329, -3.6266, -1.6925,
    -3.1023, -2.7696, -1.2755, -0.6470, -2.4143,

    -2.0107, -2.0912, -1.3053, -0.8557, -3.0683,
    -1.2872, -3.6523, -1.6703, -2.7596, -0.8063,
    -2.4633, -1.2959, -1.6153, -2.3072, -1.0705,
    -3.0543, -0.6473, -1.1650, -2.9025, -2.7710,

    -3.5519, -2.0400, -1.8667, -1.4289, -0.8050,
    -1.4602, -0.7452, -1.5754, -3.1624, -3.1247,
    -1.4677, -1.2725, -2.9575, -1.8883, -1.2513,
    -1.2164, -1.5894, -2.2217, -2.3714, -1.2110,

    -2.0843, -0.6515, -1.4252, -2.9402, -2.7964,
    -1.5261, -2.5471, -1.7167, -1.9846, -0.9488,
    -1.4847, -1.7093, -1.4095, -1.7293, -1.7675,
    -0.9203, -4.2299, -1.8740, -1.4076, -1.6671,

    -1.9052, -0.8330, -2.1839, -2.2459, -1.6193,
    -2.9108, -1.2114, -1.4616, -1.7297, -1.4330,
    -2.2656, -0.7878, -1.8533, -1.8711, -2.0349,
    -2.2457, -2.1395, -1.4509, -0.7538, -2.6381,

    -0.8078, -2.1054, -2.6703, -1.1108, -3.3867,
    -1.7774, -1.8426, -1.9473, -1.3293, -1.3273,
    -1.3490, -1.9842, -2.5357, -2.2161, -0.8800,
    -1.5412, -1.8003, -2.7603, -0.8606, -2.0066,

    -1.8342, -2.2741, -1.8348, -1.5833, -0.9877,
    -3.5196, -2.3361, -0.9124, -0.9307, -2.5531,
    -1.4862, -1.2153, -1.4453, -3.4462, -1.5625,
    -2.6455, -1.4153, -1.3079, -1.1568, -2.2897&#125;;
  std::vector<int64_t> targetsHostData = &#123;
    1, 2, 1, 1, 2, 4, 1,
    2, 2, 2, 2, 2, 2, 3,
    4, 2, 1, 4, 3, 1, 4,
    4, 1, 4, 2, 2, 2, 3&#125;;

  std::vector<float> negLoglikelihoodHostData = &#123;10.1999, 16.1340, 14.9006,  9.3596&#125;;
  std::vector<float> logAlphaHostData = &#123;
    -1.0894, -2.7162, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999, -99999,
    -4.8653, -2.2842, -6.4921, -3.9711, -99999, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -5.2121, -4.7967, -2.6162, -4.1742, -4.3179, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -6.0987, -5.0438, -3.3957, -6.7671, -4.4369, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -7.3173, -5.5735, -4.4384, -6.1313, -5.5627, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -8.9557, -6.6720, -5.7981, -6.1973, -6.7523, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -10.9664, -8.6661, -7.4600, -6.3671, -7.7544, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -14.5183, -10.6106, -10.7501, -7.8722, -9.6961, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -16.6026, -11.2422, -12.0691, -9.1833, -9.8069, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
    -18.5078, -12.0705, -12.7846, -11.1988, -10.6593, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,

    -2.0684, -2.0866, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
    -3.4510, -3.2370, -3.4692, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -4.4051, -4.7144, -3.6073, -5.5385, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -5.6836, -7.1669, -4.6003, -6.7841, -6.8170, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -7.9975, -8.2040, -6.8402, -7.2185, -8.4212, -9.5419, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -11.0430, -9.9362, -9.6580, -8.8523, -10.0013, -10.6729, -12.5874,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -12.3302, -11.3209, -10.3815, -10.1533, -9.8642, -11.2589, -11.8226,-14.2577, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -13.7904, -12.5855, -11.5118, -11.1431, -10.7654, -11.2181, -12.2686,-13.3140, -15.7179, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -15.3165, -14.0400, -12.7439, -12.3341, -11.7695, -11.9899, -12.4443,-13.6840, -14.7536, -17.4346, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -18.2273, -15.2555, -15.4129, -13.2866, -14.2301, -12.6421, -14.4091,-13.6517, -16.2998, -16.1490, -20.3454, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,

    -2.4423, -2.5942, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
    -4.0739, -3.6831, -4.2258, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -7.6125, -6.4925, -6.7635, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -9.1100, -8.3040, -7.4232, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -9.8243, -9.0682, -7.7908, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -12.2918, -10.3758, -10.0124, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -14.7551, -11.3089, -11.9478, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -16.2228, -12.5289, -12.3528, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -17.7075, -14.2718, -13.2285, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -19.9731, -16.2750, -15.1923, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,

    -2.4670, -0.7248, -99999, -99999, -99999, -99999, -99999,-99999, -99999, -99999, -99999, -99999, -99999, -99999,-99999, -99999,
    -4.3939, -2.4581, -2.6517, -2.9598, -99999, -99999, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -5.5419, -5.5142, -3.0051, -3.3784, -4.1078, -6.1507, -99999,-99999, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -7.8955, -6.9286, -5.2805, -4.5115, -5.3385, -5.0374, -8.5043,-7.6488, -99999, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -9.5920, -7.5617, -6.8010, -6.0444, -5.8452, -4.7597, -6.7031,-7.3228, -9.3453, -99999, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -12.6943, -9.8527, -9.5199, -8.2901, -8.3490, -6.6950, -7.7281,-5.8361, -10.3008, -10.6208, -99999, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -15.7486, -12.5670, -12.0336, -8.5306, -10.6803, -9.1337, -9.4448,-6.5472, -8.8790, -10.9199, -13.6751, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -16.9650, -13.7373, -12.7884, -10.0734, -9.6368, -9.2326, -9.8004,-8.6463, -7.6709, -10.9785, -12.0746, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -17.8853, -15.3655, -13.3814, -14.2154, -10.0586, -10.1583, -9.7039,-9.8935, -8.2713, -9.5090, -11.6105, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    -20.1310, -17.9262, -15.4983, -15.0687, -12.2888, -12.0440, -11.4581,-10.2538, -10.3368, -9.4675, -11.6393, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000,
    0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,0.0000, 0.0000&#125;;

  std::vector<float> outHostData = &#123;
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,

    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0,
    0, 0, 0, 0, 0
  &#125;;
  // 创建gradOut aclTensor
  ret = CreateAclTensor(gradOutHostData, gradOutShape, &gradOutDeviceAddr, aclDataType::ACL_FLOAT, &gradOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建logProbs aclTensor
  ret = CreateAclTensor(logProbsHostData, logProbsShape, &logProbsDeviceAddr, aclDataType::ACL_FLOAT, &logProbs);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建targets aclTensor
  ret = CreateAclTensor(targetsHostData, targetsShape, &targetsDeviceAddr, aclDataType::ACL_INT64, &targets);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  std::vector<int64_t> inputLengthsSizeData = &#123;10,10,10,10&#125;;
  inputLengths = aclCreateIntArray(inputLengthsSizeData.data(), 4);
  CHECK_RET(inputLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
  std::vector<int64_t> targetLengthsSizeData = &#123;2, 3, 1, 5&#125;;
  targetLengths = aclCreateIntArray(targetLengthsSizeData.data(), 4);
  CHECK_RET(targetLengths != nullptr, return ACL_ERROR_BAD_ALLOC);
  // 创建negLoglikelihood aclTensor
  ret = CreateAclTensor(negLoglikelihoodHostData, negLoglikelihoodShape, &negLoglikelihoodDeviceAddr, aclDataType::ACL_FLOAT, &negLoglikelihood);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建logAlpha aclTensor
  ret = CreateAclTensor(logAlphaHostData, logAlphaShape, &logAlphaDeviceAddr, aclDataType::ACL_FLOAT, &logAlpha);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建out aclTensor
  ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3.调用CANN算子库API，需要修改为具体的算子接口
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnCtcLossBackward第一段接口
  ret = aclnnCtcLossBackwardGetWorkspaceSize(gradOut, logProbs, targets, inputLengths, targetLengths, negLoglikelihood, logAlpha, 0, false, out, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossBackwardGetWorkspaceSize 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) &#123;
    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;);
  &#125;
  // 调用aclnnCtcLossBackward第二段接口
  ret = aclnnCtcLossBackward(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCtcLossBackward 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out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(outShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(float), 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++) &#123;
    LOG_PRINT("out result[%ld] is: %f\n", i, resultData[i]);
  &#125;

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(gradOut);
  aclDestroyTensor(logProbs);
  aclDestroyTensor(targets);
  aclDestroyIntArray(inputLengths);
  aclDestroyIntArray(targetLengths);
  aclDestroyTensor(negLoglikelihood);
  aclDestroyTensor(logAlpha);
  aclDestroyTensor(out);
  return 0;
&#125;

父主题： NN类算子接口

aclnnCummax​

接口原型​

每个算子有两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。两段式接口如下：

  • **第一段接口：**aclnnStatus aclnnCummaxGetWorkspaceSize(const aclTensor *self, int64_t dim, aclTensor *valuesOut, aclTensor* indicesOut, uint64_t *workspaceSize, aclOpExecutor **executor)
  • **第二段接口：**aclnnStatus aclnnCummax(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

功能描述​

  • 算子功能：计算张量self中的累积最大值，并返回该值以及对应的索引。

    例如valuesOut[1]为self[0]、self[1]之间的最大值，indicesOut[1]为该最大值的索引；valueOut[2]为self[0]、self[1]、self[2]之间的最大值，indicesOut[2]为该最大值索引。

  • 计算公式：

aclnnCummaxGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnCummaxGetWorkspaceSize(const aclTensor *self, int64_t dim, aclTensor *valuesOut, aclTensor* indicesOut, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • self：Device侧的aclTensor，输入张量，数据类型支持FLOAT、FLOAT16、DOUBLE、INT32、INT64、INT16、INT8、UINT8、BOOL，支持非连续的Tensor，数据格式支持ND，且shape需要与valuesOut、indicesOut一致。
    • dim：Host侧INT64类型，指定要进行最大值计算的维度，取值范围[-self.dim(), self.dim()）。
    • valuesOut：Device侧的aclTensor，输出的最大值，数据类型支持FLOAT、FLOAT16、DOUBLE、INT32、INT64、INT16、INT8、UINT8、BOOL，支持非连续的Tensor，数据格式支持ND，且shape需要与self、indicesOut一致。
    • indicesOut：Device侧的aclTensor，输出的最大值索引，数据类型支持INT32、INT64，支持非连续的Tensor，数据格式支持ND，且shape需要与self、valuesOut一致。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的self、valuesOut、indicesOut是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • self、valuesOut、indicesOut的数据类型不在支持的范围之内。
      • self、valuesOut、indicesOut的shape不在支持的范围之内。
      • 当self为0维时，不支持传入dim。
      • 输入的dim值不合法。 :::

aclnnCummax​

  • 接口定义：

    aclnnStatus aclnnCummax(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnCummaxGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例​

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

#define CHECK_RET(cond, return_expr) \
  do &#123;                               \
    if (!(cond)) &#123;                   \
      return_expr;                   \
    &#125;                                \
  &#125; while (0)

#define LOG_PRINT(message, ...)     \
  do &#123;                              \
    printf(message, ##__VA_ARGS__); \
  &#125; while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) &#123;
  int64_t shapeSize = 1;
  for (auto i : shape) &#123;
    shapeSize *= i;
  &#125;
  return shapeSize;
&#125;

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) &#123;
  // 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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;
&#125;

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) &#123;
  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--) &#123;
    strides[i] = shape[i + 1] * strides[i + 1];
  &#125;

  // 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;
&#125;

int main() &#123;
  // 1. （固定写法）device/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  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> selfShape = &#123;8&#125;;
  std::vector<int64_t> valuesOutShape = &#123;8&#125;;
  std::vector<int64_t> indicesOutShape = &#123;8&#125;;
  void* selfDeviceAddr = nullptr;
  void* valuesOutDeviceAddr = nullptr;
  void* indicesOutDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* valuesOut = nullptr;
  aclTensor* indicesOut = nullptr;
  std::vector<float> selfHostData = &#123;3.0, 3.0, 2.0, 1.0, 3.0, 2.0, 6.0, 7.0&#125;;
  std::vector<float> valuesOutHostData(8, 0.0);
  std::vector<int64_t> indicesOutHostData(8, 0);
  int64_t dim = 0;
  // 创建self aclTensor
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建valuesOut aclTensor
  ret = CreateAclTensor(valuesOutHostData, valuesOutShape, &valuesOutDeviceAddr, aclDataType::ACL_FLOAT, &valuesOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建indicesOut aclTensor
  ret = CreateAclTensor(indicesOutHostData, indicesOutShape, &indicesOutDeviceAddr, aclDataType::ACL_INT64, &indicesOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3. 调用CANN算子库API，需要修改为具体的API名称
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnCummax第一段接口
  ret = aclnnCummaxGetWorkspaceSize(self, dim, valuesOut, indicesOut, &workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCummaxGetWorkspaceSize 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) &#123;
    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);
  &#125;
  // 调用aclnnCummax第二段接口
  ret = aclnnCummax(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCummax 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
  // 获取valuesOut
  auto size = GetShapeSize(valuesOutShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), valuesOutDeviceAddr,
                    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++) &#123;
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  &#125;
  // 获取indicesOut
  auto indicesSize = GetShapeSize(indicesOutShape);
  std::vector<int64_t> indicesResultData(indicesSize, 0);
  ret = aclrtMemcpy(indicesResultData.data(), indicesResultData.size() * sizeof(indicesResultData[0]), indicesOutDeviceAddr,
                    indicesSize * sizeof(indicesResultData[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 < indicesSize; i++) &#123;
    LOG_PRINT("result[%ld] is: %ld\n", i, indicesResultData[i]);
  &#125;

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(self);
  aclDestroyTensor(valuesOut);
  aclDestroyTensor(indicesOut);

  // 7. 释放divice 资源，需要根据具体API的接口定义修改
  aclrtFree(selfDeviceAddr);
  aclrtFree(valuesOutDeviceAddr);
  aclrtFree(indicesOutDeviceAddr);
  if (workspaceSize > 0) &#123;
    aclrtFree(workspaceAddr);
  &#125;
  aclrtDestroyStream(stream);
  aclrtDestroyContext(context);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
&#125;

父主题： NN类算子接口

aclnnCummin​

接口原型​

每个算子有两段接口，必须先调用“aclnnXxxGetWorkspaceSize”接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnXxx”接口执行计算。两段式接口如下：

  • **第一段接口：**aclnnStatus aclnnCumminGetWorkspaceSize(const aclTensor *self, int64_t dim, aclTensor *valuesOut, aclTensor* indicesOut, uint64_t *workspaceSize, aclOpExecutor **executor)
  • **第二段接口：**aclnnStatus aclnnCummin(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

功能描述​

  • 算子功能：计算张量self中的累积最小值，并返回该值以及对应的索引。

    例如valuesOut[1]为self[0]、self[1]之间的最小值，indicesOut[1]为该最小值的索引；valueOut[2]为self[0]、self[1]、self[2]之间的最小值，indicesOut[2]为该最小值索引。

  • 计算公式：

aclnnCumminGetWorkspaceSize​

  • 接口定义：

    aclnnStatus aclnnCumminGetWorkspaceSize(const aclTensor *self, int64_t dim, aclTensor *valuesOut, aclTensor* indicesOut, uint64_t *workspaceSize, aclOpExecutor **executor)

  • 参数说明：

    • self：Device侧的aclTensor，输入张量，数据类型支持FLOAT、FLOAT16、DOUBLE、INT32、INT64、INT16、INT8、UINT8、BOOL，支持非连续的Tensor，数据格式支持ND，且shape需要与valuesOut、indicesOut一致。
    • dim：Host侧INT64类型，指定要进行最小值计算的维度，取值范围[-self.dim(), self.dim()）。
    • valuesOut：Device侧的aclTensor，输出的最小值，数据类型支持FLOAT、FLOAT16、DOUBLE、INT32、INT64、INT16、INT8、UINT8、BOOL，支持非连续的Tensor，数据格式支持ND，且shape需要与self、indicesOut一致。
    • indicesOut：Device侧的aclTensor，输出的最小值索引，数据类型支持INT32、INT64，支持非连续的Tensor，数据格式支持ND，且shape需要与self、valuesOut一致。
    • workspaceSize：返回用户需要在Device侧申请的workspace大小。
    • executor：返回op执行器，包含了算子计算流程。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

    :::note 说明 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）：传入的self、valuesOut、indicesOut是空指针。
    • 返回161002（ACLNN_ERR_PARAM_INVALID）：
      • self、valuesOut、indicesOut的数据类型不在支持的范围之内。
      • self、valuesOut、indicesOut的shape不在支持的范围之内。
      • 当self为0维时，不支持传入dim。
      • 输入的dim值不合法。 :::

aclnnCummin​

  • 接口定义：

    aclnnStatus aclnnCummin(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)

  • 参数说明：

    • workspace：在Device侧申请的workspace内存起址。
    • workspaceSize：在Device侧申请的workspace大小，由第一段接口aclnnCumminGetWorkspaceSize获取。
    • executor：op执行器，包含了算子计算流程。
    • stream：指定执行任务的AscendCL stream流。

  • 返回值：

    返回aclnnStatus状态码，具体参见aclnn返回码。

调用示例​

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

#define CHECK_RET(cond, return_expr) \
  do &#123;                               \
    if (!(cond)) &#123;                   \
      return_expr;                   \
    &#125;                                \
  &#125; while (0)

#define LOG_PRINT(message, ...)     \
  do &#123;                              \
    printf(message, ##__VA_ARGS__); \
  &#125; while (0)

int64_t GetShapeSize(const std::vector<int64_t>& shape) &#123;
  int64_t shape_size = 1;
  for (auto i : shape) &#123;
    shape_size *= i;
  &#125;
  return shape_size;
&#125;

int Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) &#123;
  // 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 = aclrtCreateContext(context, deviceId);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateContext failed. ERROR: %d\n", ret); return ret);
  ret = aclrtSetCurrentContext(*context);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetCurrentContext 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;
&#125;

template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
                    aclDataType dataType, aclTensor** tensor) &#123;
  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--) &#123;
    strides[i] = shape[i + 1] * strides[i + 1];
  &#125;

  // 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;
&#125;

int main() &#123;
  // 1. （固定写法）device/context/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;
  aclrtContext context;
  aclrtStream stream;
  auto ret = Init(deviceId, &context, &stream);
  // check根据自己的需要处理
  CHECK_RET(ret == 0, 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> selfShape = &#123;2,4&#125;;
  std::vector<int64_t> valuesOutShape = &#123;2,4&#125;;
  std::vector<int64_t> indicesOutShape = &#123;2,4&#125;;
  void* selfDeviceAddr = nullptr;
  void* valuesOutDeviceAddr = nullptr;
  void* indicesOutDeviceAddr = nullptr;
  aclTensor* self = nullptr;
  aclTensor* valuesOut = nullptr;
  aclTensor* indicesOut = nullptr;
  std::vector<float> selfHostData = &#123;3.0, 3.0, 2.0, 1.0, 4.0, 2.0, 6.0, 7.0&#125;;
  std::vector<float> valuesOutHostData(8, 0.0);
  std::vector<int64_t> indicesOutHostData(8, 0);
  int64_t dim = 0;
  // 创建self aclTensor
  ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
  CHECK_RET(ret == ACL_SUCCESS, return ret);
  // 创建valuesOut aclTensor
  ret = CreateAclTensor(valuesOutHostData, valuesOutShape, &valuesOutDeviceAddr, aclDataType::ACL_FLOAT, &valuesOut);
  // 创建indicesOut aclTensor
  ret = CreateAclTensor(indicesOutHostData, indicesOutShape, &indicesOutDeviceAddr, aclDataType::ACL_INT64, &indicesOut);
  CHECK_RET(ret == ACL_SUCCESS, return ret);

  // 3.调用CANN算子库API，需要修改为具体的算子接口
  uint64_t workspaceSize = 0;
  aclOpExecutor* executor;
  // 调用aclnnCummin第一段接口
  ret = aclnnCumminGetWorkspaceSize(self, dim, valuesOut, indicesOut,&workspaceSize, &executor);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCumminGetWorkspaceSize 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) &#123;
    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;);
  &#125;
  // 调用aclnnCummin第二段接口
  ret = aclnnCummin(workspaceAddr, workspaceSize, executor, stream);
  CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnCummin 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
  // 获取 valuesOutShape
  auto size = GetShapeSize(valuesOutShape);
  std::vector<float> resultData(size, 0);
  ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]), valuesOutDeviceAddr, 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++) &#123;
    LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
  &#125;
  // 获取 valuesOutShape
  auto indicesSize = GetShapeSize(indicesOutShape);
  std::vector<int64_t> indicesResultData(indicesSize, 0);
  ret = aclrtMemcpy(indicesResultData.data(), indicesResultData.size() * sizeof(indicesResultData[0]), indicesOutDeviceAddr,
                    indicesSize * sizeof(indicesResultData[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 < indicesSize; i++) &#123;
    LOG_PRINT("result[%ld] is: %ld\n", i, indicesResultData[i]);
  &#125;

  // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
  aclDestroyTensor(self);
  aclDestroyTensor(valuesOut);
  aclDestroyTensor(indicesOut);

  // 7. 释放device资源，需要根据具体API的接口定义修改
  aclrtFree(selfDeviceAddr);
  aclrtFree(valuesOutDeviceAddr);
  aclrtFree(indicesOutDeviceAddr);
  if (workspaceSize > 0) &#123;
    aclrtFree(workspaceAddr);
  &#125;
  aclrtDestroyStream(stream);
  aclrtDestroyContext(context);
  aclrtResetDevice(deviceId);
  aclFinalize();
  return 0;
&#125;

父主题： NN类算子接口

在线提单