# aclnnForeachLog ## 支持的产品型号 - 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](common/两段式接口.md)，必须先调用“aclnnForeachLogGetWorkspaceSize” 接口获取入参并根据计算流程计算所需workspace大小，再调用“aclnnForeachLog”接口执行计算。 - `aclnnStatus aclnnForeachLogGetWorkspaceSize(const aclTensorList *x, aclTensorList *out, uint64_t *workspaceSize, aclOpExecutor **executor)` - `aclnnStatus aclnnForeachLog(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)` ## 功能描述 - 算子功能：返回一个和输入张量列表同样形状大小的新张量列表，按元素以e为底做对数函数运算。 - 计算公式： $$ x = [{x_0}, {x_1}, ... {x_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\ $$ $$ {\rm y}_i = {\rm log}_e(x_i) (i=0,1,...n-1) $$ ## aclnnForeachLogGetWorkspaceSize - **参数说明**： - x（aclTensorList*，计算输入）：公式中的`x`，Device侧的aclTensorList，数据类型支持FLOAT、FLOAT16、BFLOAT16。shape维度不高于8维，[数据格式](common/数据格式.md)支持ND。支持[非连续的Tensor](common/非连续的Tensor.md)。shape与出参`out`的shape一致。 - out（aclTensorList*，计算输出）：公式中的`y`，Device侧的aclTensorList，数据类型支持FLOAT、FLOAT16、BFLOAT16。shape维度不高于8维，[数据格式](common/数据格式.md)支持ND。支持[非连续的Tensor](common/非连续的Tensor.md)。数据类型、数据格式和shape与入参`x`的数据类型、数据格式和shape一致。 - workspaceSize（uint64_t\*，出参）：返回用户需要在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](common/aclnn返回码.md)。 ``` 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x、out是空指针。返回161002（ACLNN_ERR_PARAM_INVALID）: 1. x和out的数据类型不在支持的范围之内。 2. x和out无法做数据类型推导。 ``` ## aclnnForeachLog - **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aclnnForeachLogGetWorkspaceSize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](common/aclnn返回码.md)。 ## 约束与限制无。 ## 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](common/编译与运行样例.md)。 ```Cpp #include #include #include "acl/acl.h" #include "aclnnop/aclnn_foreach_log.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& shape) { int64_t shapeSize = 1; for (auto i : shape) { shapeSize *= i; } return shapeSize; } int Init(int32_t deviceId, aclrtStream *stream) { // 固定写法，acl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 int CreateAclTensor(const std::vector& hostData, const std::vector& 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侧数据复制到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 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_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 selfShape1 = {2, 3}; std::vector selfShape2 = {1, 3}; std::vector outShape1 = {2, 3}; std::vector outShape2 = {1, 3}; void* input1DeviceAddr = nullptr; void* input2DeviceAddr = nullptr; void* out1DeviceAddr = nullptr; void* out2DeviceAddr = nullptr; aclTensor* input1 = nullptr; aclTensor* input2 = nullptr; aclTensor* out1 = nullptr; aclTensor* out2 = nullptr; std::vector input1HostData = {1, 2, 3, 4, 5, 6}; std::vector input2HostData = {7, 8, 9}; std::vector out1HostData(6, 0); std::vector out2HostData(3, 0); // 创建input1 aclTensor ret = CreateAclTensor(input1HostData, selfShape1, &input1DeviceAddr, aclDataType::ACL_FLOAT, &input1); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建input2 aclTensor ret = CreateAclTensor(input2HostData, selfShape2, &input2DeviceAddr, aclDataType::ACL_FLOAT, &input2); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建out1 aclTensor ret = CreateAclTensor(out1HostData, outShape1, &out1DeviceAddr, aclDataType::ACL_FLOAT, &out1); CHECK_RET(ret == ACL_SUCCESS, return ret); // 创建out2 aclTensor ret = CreateAclTensor(out2HostData, outShape2, &out2DeviceAddr, aclDataType::ACL_FLOAT, &out2); CHECK_RET(ret == ACL_SUCCESS, return ret); std::vector tempInput{input1, input2}; aclTensorList* tensorListInput = aclCreateTensorList(tempInput.data(), tempInput.size()); std::vector tempOutput{out1, out2}; aclTensorList* tensorListOutput = aclCreateTensorList(tempOutput.data(), tempOutput.size()); // 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aclnnForeachLog第一段接口 ret = aclnnForeachLogGetWorkspaceSize(tensorListInput, tensorListOutput, &workspaceSize, &executor); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachLogGetWorkspaceSize 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aclnnForeachLog第二段接口 ret = aclnnForeachLog(workspaceAddr, workspaceSize, executor, stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnForeachLog 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侧内存上的结果复制至host侧，需要根据具体API的接口定义修改 auto size = GetShapeSize(outShape1); std::vector out1Data(size, 0); ret = aclrtMemcpy(out1Data.data(), out1Data.size() * sizeof(out1Data[0]), out1DeviceAddr, size * sizeof(out1Data[0]), ACL_MEMCPY_DEVICE_TO_HOST); for (int64_t i = 0; i < size; i++) { LOG_PRINT("out1 result[%ld] is: %f\n", i, out1Data[i]); } size = GetShapeSize(outShape2); std::vector out2Data(size, 0); ret = aclrtMemcpy(out2Data.data(), out2Data.size() * sizeof(out2Data[0]), out2DeviceAddr, size * sizeof(out2Data[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("out2 result[%ld] is: %f\n", i, out2Data[i]); } // 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改 aclDestroyTensorList(tensorListInput); aclDestroyTensorList(tensorListOutput); // 7.释放device资源，需要根据具体API的接口定义修改 aclrtFree(input1DeviceAddr); aclrtFree(input2DeviceAddr); aclrtFree(out1DeviceAddr); aclrtFree(out2DeviceAddr); if (workspaceSize > 0) { aclrtFree(workspaceAddr); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; } ```