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产品支持情况产品是否支持Atlas 200I/500 A2 推理产品×Atlas 推理系列产品×Atlas 训练系列产品×Atlas A3 训练系列产品/Atlas A3 推理系列产品√Atlas A2 训练系列产品/Atlas A2 推理系列产品√Ascend 950PR/Ascend 950DT×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$$ resultA \odot\ B (A){ij}(B){ij} $$示例输入“A”为[ [ 11i, 11i ],[ 22i, 22i ] ]输入“B”为[ [ 11i, 11i ],[ 22i, 22i ] ]调用asdMul算子后输出“result”为[ [ 02i, 02i ],[ 08i, 08i ] ]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nint输入表示输入的元素个数。xvoid *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]yvoid *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]zvoid *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]streamvoid *输入npu执行流。workspacevoid *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[19.22e18]。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::vectorint64_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::vectorT hostData, const std::vectorint64_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::vectorint64_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::complexop::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::vectorstd::complexop::fp16_t tensorInXData; std::vectorstd::complexop::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::vectorstd::complexop::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::vectorint64_t xShape {vecSize}; std::vectorint64_t yShape {vecSize}; std::vectorint64_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::complexop::fp16_t), outputZDeviceAddr, vecSize * sizeof(std::complexop::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::vectorint64_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::vectorT hostData, const std::vectorint64_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::vectorint64_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::complexfloat *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::vectorstd::complexfloat tensorInXData; std::vectorstd::complexfloat 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::vectorstd::complexfloat 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::vectorint64_t xShape {vecSize}; std::vectorint64_t yShape {vecSize}; std::vectorint64_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::complexfloat), outputZDeviceAddr, vecSize * sizeof(std::complexfloat), 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创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考