FFT_1D
C2C
C2C_1D算子调用示例:
#include <iostream> #include <vector> #include "asdsip.h" #include "acl/acl.h" #include "aclnn/acl_meta.h" using namespace AsdSip; #define CHECK_RET(cond, return_expr) \ do { \ if (!(cond)) { \ return_expr; \ } \ } while (0) #define LOG_PRINT(message, ...) \ do { \ printf(message, ##__VA_ARGS__); \ } while (0) #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) 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,AscendCL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() { 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); // 创造tensor的Host侧数据 int batch = 32, Nfft = 128; // c2c dft // int batch = 32, Nfft = 8192; // c2c fftb // int batch = 32, Nfft = 15000; // c2c mixed // int batch = 32, Nfft = 32768; // c2c fftn // int batch = 32, Nfft = 199 * 199; // core any const int64_t tensorInSize = batch * Nfft; std::vector<int64_t> selfShape = {batch, Nfft}; std::vector<int64_t> outShape = {batch, Nfft}; std::vector<std::complex<float>> inputHostData(tensorInSize, std::complex<float>(0, 0)); for (int i = 0; i < tensorInSize; i++) { inputHostData[i] = std::complex<float>(i, i + 1); } std::vector<std::complex<float>> outHostData(tensorInSize, std::complex<float>(0, 0)); void *inputDeviceAddr = nullptr; void *outDeviceAddr = nullptr; aclTensor *input = nullptr; aclTensor *out = nullptr; ret = CreateAclTensor(inputHostData, selfShape, &inputDeviceAddr, aclDataType::ACL_COMPLEX64, &input); CHECK_RET(ret == ACL_SUCCESS, return ret); ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_COMPLEX64, &out); CHECK_RET(ret == ACL_SUCCESS, return ret); asdFftHandle handle; asdFftCreate(handle); asdFftMakePlan1D(handle, Nfft, asdFftType::ASCEND_FFT_C2C, asdFftDirection::ASCEND_FFT_FORWARD, batch); size_t work_size; asdFftGetWorkspaceSize(handle, work_size); void *workspaceAddr = nullptr; if (work_size > 0) { ret = aclrtMalloc(&workspaceAddr, static_cast<int64_t>(work_size), ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret); } asdFftSetWorkspace(handle, (uint8_t *)workspaceAddr); asdFftSetStream(handle, stream); ASD_STATUS_CHECK(asdFftExecC2C(handle, input, out)); ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); asdFftDestroy(handle); auto size = GetShapeSize(outShape); std::vector<std::complex<float>> outData(size, 0); ret = aclrtMemcpy(outData.data(), outData.size() * sizeof(outData[0]), outDeviceAddr, size * sizeof(outData[0]), ACL_MEMCPY_DEVICE_TO_HOST); // 打印输出tensor值中前16个 for (int64_t i = 0; i < 16; i++) { std::cout << static_cast<std::complex<float>>(outData[i]) << "\t"; } std::cout << "\nend result" << std::endl; std::cout << "Execute successfully." << std::endl; aclDestroyTensor(input); aclDestroyTensor(out); aclrtFree(inputDeviceAddr); aclrtFree(outDeviceAddr); if (work_size > 0) { aclrtFree(workspaceAddr); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }
C2R
C2R_1D算子调用示例:
#include <iostream> #include <vector> #include "asdsip.h" #include "acl/acl.h" #include "aclnn/acl_meta.h" using namespace AsdSip; #define CHECK_RET(cond, return_expr) \ do { \ if (!(cond)) { \ return_expr; \ } \ } while (0) #define LOG_PRINT(message, ...) \ do { \ printf(message, ##__VA_ARGS__); \ } while (0) #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) 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,AscendCL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() { 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); // 创造tensor的Host侧数据 int batch = 32, Nfft = 128; // int batch = 32, Nfft = 8192; // int batch = 8, Nfft = 567; // int batch = 32, Nfft = 997; // int batch = 32, Nfft = 15000; // 创造tensor的Host侧数据 // int batch = 32, Nfft = 199 * 199; const int64_t inSignal = Nfft / 2 + 1; const int64_t outSignal = Nfft; const int64_t tensorInSize = batch * inSignal; const int64_t tensorOutSize = batch * outSignal; std::vector<int64_t> selfShape = {batch, inSignal}; std::vector<int64_t> outShape = {batch, outSignal}; std::vector<std::complex<float>> inputHostData(tensorInSize, std::complex<float>(0, 0)); for (int i = 0; i < tensorInSize; i++) { inputHostData[i] = std::complex<float>(i, i + 1); } std::vector<float> outHostData(tensorOutSize, 0); void *inputDeviceAddr = nullptr; void *outDeviceAddr = nullptr; aclTensor *input = nullptr; aclTensor *out = nullptr; ret = CreateAclTensor(inputHostData, selfShape, &inputDeviceAddr, aclDataType::ACL_COMPLEX64, &input); CHECK_RET(ret == ACL_SUCCESS, return ret); ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out); CHECK_RET(ret == ACL_SUCCESS, return ret); asdFftHandle handle; asdFftCreate(handle); asdFftMakePlan1D(handle, Nfft, asdFftType::ASCEND_FFT_C2R, asdFftDirection::ASCEND_FFT_FORWARD, batch); size_t work_size; asdFftGetWorkspaceSize(handle, work_size); void *workspaceAddr = nullptr; if (work_size > 0) { ret = aclrtMalloc(&workspaceAddr, static_cast<int64_t>(work_size), ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret); } asdFftSetWorkspace(handle, (uint8_t *)workspaceAddr); asdFftSetStream(handle, stream); ASD_STATUS_CHECK(asdFftExecC2R(handle, input, out)); ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); asdFftDestroy(handle); auto size = GetShapeSize(outShape); std::vector<float> outData(size, 0); ret = aclrtMemcpy(outData.data(), outData.size() * sizeof(outData[0]), outDeviceAddr, size * sizeof(outData[0]), ACL_MEMCPY_DEVICE_TO_HOST); // 打印输出tensor值中前16个 for (int64_t i = 0; i < 16; i++) { std::cout << static_cast<float>(outData[i]) << "\t"; } std::cout << "\nend result" << std::endl; std::cout << "Execute successfully." << std::endl; aclDestroyTensor(input); aclDestroyTensor(out); aclrtFree(inputDeviceAddr); aclrtFree(outDeviceAddr); if (work_size > 0) { aclrtFree(workspaceAddr); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }
R2C
R2C_1D算子调用示例:
#include <iostream> #include <vector> #include "asdsip.h" #include "acl/acl.h" #include "aclnn/acl_meta.h" using namespace AsdSip; #define CHECK_RET(cond, return_expr) \ do { \ if (!(cond)) { \ return_expr; \ } \ } while (0) #define LOG_PRINT(message, ...) \ do { \ printf(message, ##__VA_ARGS__); \ } while (0) #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) 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,AscendCL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() { 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); // 创造tensor的Host侧数据 int batch = 32, Nfft = 256; // int batch = 32, Nfft = 199 * 199; const int64_t inSignal = Nfft; const int64_t outSignal = Nfft / 2 + 1; const int64_t tensorInSize = batch * inSignal; const int64_t tensorOutSize = batch * outSignal; std::vector<int64_t> selfShape = {batch, inSignal}; std::vector<int64_t> outShape = {batch, outSignal}; std::vector<float> inputHostData(tensorInSize, 0); for (int i = 0; i < tensorInSize; i++) { inputHostData[i] = i; } std::vector<std::complex<float>> outHostData(tensorOutSize, std::complex<float>(0, 0)); void *inputDeviceAddr = nullptr; void *outDeviceAddr = nullptr; aclTensor *input = nullptr; aclTensor *out = nullptr; ret = CreateAclTensor(inputHostData, selfShape, &inputDeviceAddr, aclDataType::ACL_FLOAT, &input); CHECK_RET(ret == ACL_SUCCESS, return ret); ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_COMPLEX64, &out); CHECK_RET(ret == ACL_SUCCESS, return ret); asdFftHandle handle; asdFftCreate(handle); asdFftMakePlan1D(handle, Nfft, asdFftType::ASCEND_FFT_R2C, asdFftDirection::ASCEND_FFT_FORWARD, batch); size_t work_size; asdFftGetWorkspaceSize(handle, work_size); void *workspaceAddr = nullptr; if (work_size > 0) { ret = aclrtMalloc(&workspaceAddr, static_cast<int64_t>(work_size), ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret); } asdFftSetWorkspace(handle, (uint8_t *)workspaceAddr); asdFftSetStream(handle, stream); ASD_STATUS_CHECK(asdFftExecR2C(handle, input, out)); ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); asdFftDestroy(handle); auto size = GetShapeSize(outShape); std::vector<std::complex<float>> outData(size, 0); ret = aclrtMemcpy(outData.data(), outData.size() * sizeof(outData[0]), outDeviceAddr, size * sizeof(outData[0]), ACL_MEMCPY_DEVICE_TO_HOST); // 打印输出tensor值中前16个 for (int64_t i = 0; i < 16; i++) { std::cout << static_cast<std::complex<float>>(outData[i]) << "\t"; } std::cout << "\nend result" << std::endl; std::cout << "Execute successfully." << std::endl; aclDestroyTensor(input); aclDestroyTensor(out); aclrtFree(inputDeviceAddr); aclrtFree(outDeviceAddr); if (work_size > 0) { aclrtFree(workspaceAddr); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }
父主题: FFT