Ssyr
asdBlasSsyr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 = 6; float alpha = 2.0f; asdBlasFillMode_t uplo = asdBlasFillMode_t::ASDBLAS_FILL_MODE_LOWER; int64_t incx = 1; int64_t lda = n; const int64_t tensorXSize = 6; std::vector<float> tensorInXData; tensorInXData.reserve(tensorXSize); for (int i = 0; i < tensorXSize; i++) { tensorInXData.push_back(1.0 + i); } const int64_t tensorASize = n * n; std::vector<float> tensorInAData; tensorInAData.reserve(tensorASize); for (int64_t i = 0; i < n; i++) { for (int64_t j = 0; j < n; j++) { tensorInAData.push_back(2.0f + i); } } std::cout << "alpha = " << alpha << std::endl; std::cout << "uplo = " << uplo << std::endl; 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[i * n + j] << " "; std::cout << std::endl; } std::vector<int64_t> xShape = {n}; std::vector<int64_t> matAShape = {n, n}; aclTensor *inputX = nullptr; aclTensor *inputA = nullptr; void *inputXDeviceAddr = nullptr; void *inputADeviceAddr = nullptr; ret = CreateAclTensor<float>(tensorInXData, xShape, &inputXDeviceAddr, aclDataType::ACL_FLOAT, &inputX); CHECK_RET(ret == ACL_SUCCESS, return ret); ret = CreateAclTensor<float>(tensorInAData, matAShape, &inputADeviceAddr, aclDataType::ACL_FLOAT, &inputA); CHECK_RET(ret == ACL_SUCCESS, return ret); asdBlasHandle handle; asdBlasCreate(handle); size_t lwork = 0; void *buffer = nullptr; asdBlasMakeSsyrPlan(handle); 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); // A = alpha * x * x.T + A ASD_STATUS_CHECK(asdBlasSsyr(handle, uplo, n, alpha, inputX, incx, inputA, lda)); asdBlasSynchronize(handle); asdBlasDestroy(handle); ret = aclrtMemcpy(tensorInAData.data(), n * n * sizeof(float), inputADeviceAddr, n * n * 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); std::cout << "------- output A -------" << std::endl; for (int64_t i = 0; i < n; i++) { for (int64_t j = 0; j < n; j++) { std::cout << tensorInAData[i * n + j] << " "; } std::cout << std::endl; } std::cout << "Execute successfully." << std::endl; aclDestroyTensor(inputX); aclDestroyTensor(inputA); aclrtFree(inputXDeviceAddr); aclrtFree(inputADeviceAddr); aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }
父主题: BLAS