CANN/sip asdMul复数矩阵乘积算子

asdMul

【免费下载链接】sip本项目是CANN提供的一款高效、可靠的高性能信号处理算子加速库，基于华为Ascend AI处理器，专门为信号处理领域而设计。项目地址: https://gitcode.com/cann/sip

产品支持情况

功能说明

• 接口功能：支持向量逐元素乘积(Hadamard)能力，返回一个和输入同样形状大小的复数矩阵。

• 计算公式：

  $$ result=A \odot\ B =(A){ij}(B){ij} $$

  示例：

  输入“A”为：
  [ [ 1+1i, 1+1i ],
  [ 2+2i, 2+2i ] ]
  输入“B”为：
  [ [ 1+1i, 1+1i ],
  [ 2+2i, 2+2i ] ]
  调用asdMul算子后，输出“result”为：
  [ [ 0+2i, 0+2i ],
  [ 0+8i, 0+8i ] ]

函数原型

AspbStatus asdMul( int n, const void * x, const void * y, const void * z, void * stream, void * workspace = nullptr)

asdMul

• 参数说明：

  参数名	输入/输出	描述
  n（int）	输入	表示输入的元素个数。
  x（void *）	输入	表示输入的矩阵，对应公式中的'A'。 数据类型支持COMPLEX32、COMPLEX64 数据格式支持ND。 shape为[n]
  y（void *）	输入	表示输入的矩阵，对应公式中的'B'。 数据类型支持COMPLEX32、COMPLEX64 数据格式支持ND。 shape为[n]
  z（void *）	输出	表示输出的矩阵，对应公式中的'result'。 数据类型支持COMPLEX32、COMPLEX64 数据格式支持ND。 shape为[n]
  stream（void *）	输入	npu执行流。
  workspace（void *）	输入	asdMul算子所需要的workspace。

• 返回值：

  返回状态码，具体参见SiP返回码。

约束说明

• 输入的元素个数n理论支持[1，9.22e+18]。

调用示例

示例代码如下，该样例旨在提供快速上手、开发和调试算子的最小化实现，其核心目标是使用最精简的代码展示算子的核心功能，而非提供生产级的安全保障。不推荐用户直接将示例代码作为业务代码，若用户将示例代码应用在自身的真实业务场景中且发生了安全问题，则需用户自行承担。

• mul_complex32

#include <iostream> #include <vector> #include <complex> #include "asdsip.h" #include "acl/acl.h" #include "acl_meta.h" using namespace AsdSip; #define ASD_STATUS_CHECK(err) \ do { \ AsdSip::AspbStatus err_ = (err); \ if (err_ != AsdSip::ErrorType::ACL_SUCCESS) { \ std::cout << "Execute failed." << std::endl; \ exit(-1); \ } \ } while (0) #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，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 <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<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; } void printTensor(const std::complex<op::fp16_t> *tensorData, int64_t nums) { for (int64_t i = 0; i < nums; i++) { std::cout << "(" << (float)tensorData[i].real() << "," << (float)tensorData[i].imag() << ")" << " "; } std::cout << std::endl; } int main(int argc, char **argv) { int 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); int64_t n = 8; int64_t vecSize = n; std::vector<std::complex<op::fp16_t>> tensorInXData; std::vector<std::complex<op::fp16_t>> tensorInYData; tensorInXData.reserve(vecSize); tensorInYData.reserve(vecSize); for (int64_t i = 0; i < vecSize; i++) { tensorInXData.push_back({(op::fp16_t)(9.0f + i), (op::fp16_t)(100.0f + i)}); } for (int64_t i = 0; i < vecSize; i++) { tensorInYData.push_back({(op::fp16_t)(22.0f + i), (op::fp16_t)(33.0f * (i + 1))}); } std::vector<std::complex<op::fp16_t>> tensorOutZData( vecSize, {(op::fp16_t)0.0f, (op::fp16_t)0.0f}); std::cout << "------- input X -------" << std::endl; printTensor(tensorInXData.data(), vecSize); std::cout << "------- input Y -------" << std::endl; printTensor(tensorInYData.data(), vecSize); std::vector<int64_t> xShape = {vecSize}; std::vector<int64_t> yShape = {vecSize}; std::vector<int64_t> zShape = {vecSize}; aclTensor *inputX = nullptr; aclTensor *inputY = nullptr; aclTensor *outputZ = nullptr; void *inputXDeviceAddr = nullptr; void *inputYDeviceAddr = nullptr; void *outputZDeviceAddr = nullptr; ret = CreateAclTensor(tensorInXData, xShape, &inputXDeviceAddr, aclDataType::ACL_COMPLEX32, &inputX); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(tensorInYData, yShape, &inputYDeviceAddr, aclDataType::ACL_COMPLEX32, &inputY); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(tensorOutZData, zShape, &outputZDeviceAddr, aclDataType::ACL_COMPLEX32, &outputZ); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ASD_STATUS_CHECK(asdMul(n, inputX, inputY, outputZ, stream)); ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ::ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); ret = aclrtMemcpy(tensorOutZData.data(), vecSize * sizeof(std::complex<op::fp16_t>), outputZDeviceAddr, vecSize * sizeof(std::complex<op::fp16_t>), ACL_MEMCPY_DEVICE_TO_HOST); CHECK_RET(ret == ::ACL_SUCCESS, LOG_PRINT("copy z from device to host failed. ERROR: %d\n", ret); return ret); std::cout << "------- output Z -------" << std::endl; printTensor(tensorOutZData.data(), vecSize); std::cout << "Execute successfully." << std::endl; aclDestroyTensor(inputX); aclDestroyTensor(inputY); aclDestroyTensor(outputZ); aclrtFree(inputXDeviceAddr); aclrtFree(inputYDeviceAddr); aclrtFree(outputZDeviceAddr); aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }

• mul_complex64

#include <iostream> #include <vector> #include <complex> #include "asdsip.h" #include "acl/acl.h" #include "acl_meta.h" using namespace AsdSip; #define ASD_STATUS_CHECK(err) \ do { \ AsdSip::AspbStatus err_ = (err); \ if (err_ != AsdSip::ErrorType::ACL_SUCCESS) { \ std::cout << "Execute failed." << std::endl; \ exit(-1); \ } \ } while (0) #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，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 <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<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; } void printTensor(const std::complex<float> *tensorData, int64_t nums) { for (int64_t i = 0; i < nums; i++) { std::cout << tensorData[i] << " "; } std::cout << std::endl; } int main(int argc, char **argv) { int 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); int64_t n = 8; int64_t vecSize = n; std::vector<std::complex<float>> tensorInXData; std::vector<std::complex<float>> tensorInYData; tensorInXData.reserve(vecSize); tensorInYData.reserve(vecSize); for (int64_t i = 0; i < vecSize; i++) { tensorInXData[i] = {(float)(1.0 + i), (float)(1.0 + i)}; } for (int64_t i = 0; i < vecSize; i++) { tensorInYData[i] = {(float)(2.0 + i), 3.0}; } std::vector<std::complex<float>> tensorOutZData(vecSize, {0.0f, 0.0f}); std::cout << "------- input X -------" << std::endl; printTensor(tensorInXData.data(), vecSize); std::cout << "------- input Y -------" << std::endl; printTensor(tensorInYData.data(), vecSize); std::vector<int64_t> xShape = {vecSize}; std::vector<int64_t> yShape = {vecSize}; std::vector<int64_t> zShape = {vecSize}; aclTensor *inputX = nullptr; aclTensor *inputY = nullptr; aclTensor *outputZ = nullptr; void *inputXDeviceAddr = nullptr; void *inputYDeviceAddr = nullptr; void *outputZDeviceAddr = nullptr; ret = CreateAclTensor(tensorInXData, xShape, &inputXDeviceAddr, aclDataType::ACL_COMPLEX64, &inputX); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(tensorInYData, yShape, &inputYDeviceAddr, aclDataType::ACL_COMPLEX64, &inputY); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(tensorOutZData, zShape, &outputZDeviceAddr, aclDataType::ACL_COMPLEX64, &outputZ); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ASD_STATUS_CHECK(asdMul(n, inputX, inputY, outputZ, stream)); ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ::ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); ret = aclrtMemcpy(tensorOutZData.data(), vecSize * sizeof(std::complex<float>), outputZDeviceAddr, vecSize * sizeof(std::complex<float>), ACL_MEMCPY_DEVICE_TO_HOST); CHECK_RET(ret == ::ACL_SUCCESS, LOG_PRINT("copy z from device to host failed. ERROR: %d\n", ret); return ret); std::cout << "------- Output -------" << std::endl; printTensor(tensorOutZData.data(), vecSize); std::cout << "Execute successfully." << std::endl; aclDestroyTensor(inputX); aclDestroyTensor(inputY); aclDestroyTensor(outputZ); aclrtFree(inputXDeviceAddr); aclrtFree(inputYDeviceAddr); aclrtFree(outputZDeviceAddr); aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }

【免费下载链接】sip本项目是CANN提供的一款高效、可靠的高性能信号处理算子加速库，基于华为Ascend AI处理器，专门为信号处理领域而设计。项目地址: https://gitcode.com/cann/sip