Ctrmv
asdBlasCtrmv算子调用示例:
#include <iostream> #include <vector> #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::NO_ERROR) { \ 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; } 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 = 4; int64_t incx = 1; int64_t lda = 4; asdBlasFillMode_t uplo = asdBlasFillMode_t::ASDBLAS_FILL_MODE_LOWER; asdBlasOperation_t trans = asdBlasOperation_t::ASDBLAS_OP_N; asdBlasDiagType_t diag = asdBlasDiagType_t::ASDBLAS_DIAG_NON_UNIT; int64_t tensorXSize = 4; std::vector<std::complex<float>> tensorInXData; tensorInXData.reserve(tensorXSize); for (int64_t i = 0; i < tensorXSize; i++) { tensorInXData[i] = {(float)(1.0 * i), (float)(1.0 * i)}; } int64_t tensorASize = n * n; std::vector<std::complex<float>> tensorInAData; tensorInAData.reserve(tensorASize); for (int64_t i = 0; i < n; i++) { for (int64_t j = 0; j < n; j++) { tensorInAData[n * i + j] = {0.0, 0.0}; } } for (int64_t i = 0; i < n; i++) { for (int64_t j = 0; j < i + 1; j++) { tensorInAData[n * i + j] = {1.0, 2.0}; } } std::cout << "------- input x -------" << std::endl; for (int64_t i = 0; i < n; i++) { std::cout << tensorInXData[i] << " "; } std::cout << std::endl; std::cout << "------- input A -------" << std::endl; for (int64_t i = 0; i < n; i++) { for (int64_t j = 0; j < n; j++) { std::cout << tensorInAData[n * i + j] << " "; } std::cout << std::endl; } std::vector<int64_t> aShape = {tensorASize}; std::vector<int64_t> xShape = {tensorXSize}; aclTensor *inputA = nullptr; aclTensor *inputX = nullptr; void *inputADeviceAddr = nullptr; void *inputXDeviceAddr = nullptr; ret = CreateAclTensor(tensorInAData, aShape, &inputADeviceAddr, aclDataType::ACL_COMPLEX64, &inputA); CHECK_RET(ret == ACL_SUCCESS, return ret); ret = CreateAclTensor(tensorInXData, xShape, &inputXDeviceAddr, aclDataType::ACL_COMPLEX64, &inputX); CHECK_RET(ret == ACL_SUCCESS, return ret); asdBlasHandle handle; asdBlasCreate(handle); size_t lwork = 0; void *buffer = nullptr; asdBlasMakeCtrmvPlan(handle, uplo, n); asdBlasGetWorkspaceSize(handle, lwork); std::cout << "lwork = " << lwork << std::endl; if (lwork > 0) { ret = aclrtMalloc(&buffer, static_cast<int64_t>(lwork), ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret); } asdBlasSetWorkspace(handle, buffer); asdBlasSetStream(handle, stream); ASD_STATUS_CHECK(asdBlasCtrmv(handle, uplo, trans, diag, n, inputA, lda, inputX, incx)); asdBlasSynchronize(handle); asdBlasDestroy(handle); ret = aclrtMemcpy(tensorInXData.data(), tensorXSize * sizeof(std::complex<float>), inputXDeviceAddr, tensorXSize * sizeof(std::complex<float>), ACL_MEMCPY_DEVICE_TO_HOST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy tensor x from device to host failed. ERROR: %d\n", ret); return ret); std::cout << "------- result -------" << std::endl; for (int64_t i = 0; i < n; i++) { std::cout << tensorInXData[i] << " "; } std::cout << std::endl; std::cout << "Execute successfully." << std::endl; aclDestroyTensor(inputA); aclDestroyTensor(inputX); aclrtFree(inputADeviceAddr); aclrtFree(inputXDeviceAddr); aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }
父主题: BLAS