---
title: FFT_2D
description: "安全声明："
url: https://www.hiascend.com/document/detail/zh/canncommercial/latest/API/SiP/SIP_API_0059.html
sourcePath: /source/zh/canncommercial/900/API/SiP/SIP_API_0059.html
indexId: 8f95be0505d3d92cc0e6d1a73eef3e11e639070a7c18b3477799fd08f00140c055
---
# FFT_2D

安全声明：

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。

#### C2C_2D

C2C_2D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::ErrorType::ACL_SUCCESS) {                                      \
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 = 2, Nfft1 = 199 * 199, Nfft2 = 4096;
int batch = 2, Nfft1 = 64, Nfft2 = 64; // core dd
const int64_t inSignal = Nfft2;
const int64_t outSignal = Nfft2;
const int64_t tensorInSize = batch * Nfft1 * inSignal;
const int64_t tensorOutSize = batch * Nfft1 * outSignal;
std::vector<int64_t> selfShape = {batch, Nfft1, inSignal};
std::vector<int64_t> outShape = {batch, Nfft1, 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<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_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);
asdFftMakePlan2D(handle, Nfft1, Nfft2, 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_2D

C2R_2D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::ErrorType::ACL_SUCCESS) {                                      \
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 = 32, Nfft = 15000;
// 创造tensor的Host侧数据
int batch = 2, Nfft1 = 128, Nfft2 = 128;
const int64_t inSignal = Nfft2 / 2 + 1;
const int64_t outSignal = Nfft2;
const int64_t tensorInSize = batch * Nfft1 * inSignal;
const int64_t tensorOutSize = batch * Nfft1 * outSignal;
std::vector<int64_t> selfShape = {batch, Nfft1, inSignal};
std::vector<int64_t> outShape = {batch, Nfft1, 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);
asdFftMakePlan2D(handle, Nfft1, Nfft2, 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_2D

R2C_2D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::ErrorType::ACL_SUCCESS) {                                      \
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 = 2, Nfft1 = 1024, Nfft2 = 4096;
const int64_t inSignal = Nfft2;
const int64_t outSignal = Nfft2 / 2 + 1;
const int64_t tensorInSize = batch * Nfft1 * inSignal;
const int64_t tensorOutSize = batch * Nfft1 * outSignal;
std::vector<int64_t> selfShape = {batch, Nfft1, inSignal};
std::vector<int64_t> outShape = {batch, Nfft1, 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);
asdFftMakePlan2D(handle, Nfft1, Nfft2, 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;
}
```