rsInterpolationBySinc
sincTab 预处理：
1、系数矩阵要和复数点乘，在NPU上会转换为系数矩阵和两组float32相乘，所以需要将系数转为下面格式
[系数1, 0]
[ 0, 系数1]
此时虚数矩阵会被扩充成 ((quantNum + 1) * 2) * (interpNum * 2)
2、为了亲和NPU（32字节对齐），需要有四种格式矩阵，每行开头不补0，补2个0，补4个0及补6个0：
w0,0,w1,0,w2,0,w3,0,w4,0,w5,0,w6,0,w7,0,w8,0,w9,0,w10,0,w11,0,w12,0,w13,0,w14,0,w15,0,0,0,0,0,0,0,0
0,0,w0,0,w1,0,w2,0,w3,0,w4,0,w5,0,w6,0,w7,0,w8,0,w9,0,w10,0,w11,0,w12,0,w13,0,w14,0,w15,0,0,0,0,0,0
0,0,0,0,w0,0,w1,0,w2,0,w3,0,w4,0,w5,0,w6,0,w7,0,w8,0,w9,0,w10,0,w11,0,w12,0,w13,0,w14,0,w15,0,0,0,0
0,0,0,0,0,0,w0,0,w1,0,w2,0,w3,0,w4,0,w5,0,w6,0,w7,0,w8,0,w9,0,w10,0,w11,0,w12,0,w13,0,w14,0,w15,0,0
static void genTab(float *tab, int tabSize) {
auto ret = memset_s(tab, tabSize * sizeof(float), 0, tabSize * sizeof(float));
for(int i = 0; i < 4; i++) {
int zeroOffset = i * 2;
int blockOffset = i * (33 * 2) * (16 * 2 + 8);
for(int j = 0; j < 33; j++) {
int rowOffset_real = blockOffset + j * (16 * 2 + 8) * 2;
int rowOffset_imag = rowOffset_real + (16 * 2 + 8);
for(int k = 0; k < 16; k++) {
tab[rowOffset_real + zeroOffset + k * 2] = DINTER_CORE_33x16[j * 16 + k];
tab[rowOffset_imag + zeroOffset + k * 2 + 1] = DINTER_CORE_33x16[j * 16 + k];
}
}
}
}
rsInterpolationBySinc
- 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。
不推荐用户直接将样例作为业务代码，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。
- rsInterpolationBySinc算子调用示例：
#include <iostream> #include <vector> #include <securec.h> #include "asdsip.h" #include "acl/acl.h" #include "acl_meta.h" using std::complex; 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 DINTER_CORE_SIZE 528 static void genTab(float *tab, int tabSize) { static float DINTER_CORE_33x16[DINTER_CORE_SIZE]; for (int i = 0; i < DINTER_CORE_SIZE; ++i) { DINTER_CORE_33x16[i] = ((rand() / (float)RAND_MAX) * 2.0f) - 1.0f; } for (int i = 0; i < 4; i++) { int zeroOffset = i * 2; int blockOffset = i * (33 * 2) * (16 * 2 + 8); for (int j = 0; j < 33; j++) { int rowOffset_real = blockOffset + j * (16 * 2 + 8) * 2; int rowOffset_imag = rowOffset_real + (16 * 2 + 8); for (int k = 0; k < 16; k++) { tab[rowOffset_real + zeroOffset + k * 2] = DINTER_CORE_33x16[j * 16 + k]; tab[rowOffset_imag + zeroOffset + k * 2 + 1] = DINTER_CORE_33x16[j * 16 + k]; } } } } #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); int batch = 1; int signalLength = 64; int interpLength = signalLength; const int64_t tabSize = (33 * 2) * (16 * 2 + 8) * 4; // 2 ：虚实系数，8：补零，4：不补0，补2,4,6个零 const unsigned long inSize = batch * signalLength; const unsigned long posSize = batch * interpLength; const unsigned long tabIndexSize = batch * interpLength; const unsigned long outSize = batch * interpLength; float *tabDate = new float[tabSize](); genTab(tabDate, tabSize); std::vector<float> tab(tabDate, tabDate + tabSize); std::vector<complex<float>> inSignal; inSignal.reserve(inSize); for (long long ii = 0; ii < signalLength; ++ii) { inSignal[ii] = complex<float>(ii, ii); } std::vector<int32_t> intpPos; intpPos.reserve(posSize); for (long long ii = 0; ii < interpLength; ++ii) { intpPos[ii] = ii; } std::vector<int16_t> tabIndex; tabIndex.reserve(tabIndexSize); for (long long ii = 0; ii < interpLength; ++ii) { tabIndex[ii] = ii % 33; } std::vector<complex<float>> outSignal; outSignal.reserve(outSize); for (long long ii = 0; ii < interpLength; ++ii) { outSignal[ii] = complex<float>(0, 0); } aclTensor *tensorIn = nullptr; aclTensor *tensorTab = nullptr; aclTensor *tensorPos = nullptr; aclTensor *tensorTabIndex = nullptr; aclTensor *tensorOut = nullptr; void *tensorInDeviceAddr = nullptr; void *tensorTabDeviceAddr = nullptr; void *tensorPosDeviceAddr = nullptr; void *tensorTabIndexDeviceAddr = nullptr; void *tensorOutDeviceAddr = nullptr; ret = CreateAclTensor(inSignal, {batch, signalLength}, &tensorInDeviceAddr, aclDataType::ACL_COMPLEX64, &tensorIn); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(tab, {1, tabSize}, &tensorTabDeviceAddr, aclDataType::ACL_FLOAT, &tensorTab); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(intpPos, {batch, interpLength}, &tensorPosDeviceAddr, aclDataType::ACL_INT32, &tensorPos); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor( tabIndex, {batch, interpLength}, &tensorTabIndexDeviceAddr, aclDataType::ACL_INT16, &tensorTabIndex); CHECK_RET(ret == ::ACL_SUCCESS, return ret); ret = CreateAclTensor(outSignal, {batch, interpLength}, &tensorOutDeviceAddr, aclDataType::ACL_COMPLEX64, &tensorOut); CHECK_RET(ret == ::ACL_SUCCESS, return ret); void *workspace = nullptr; size_t workspaceSize = 0; rsInterpolationBySincGetWorkspaceSize(workspaceSize); if (workspaceSize > 0) { ret = aclrtMalloc(&workspace, static_cast<int64_t>(workspaceSize), ACL_MEM_MALLOC_HUGE_FIRST); CHECK_RET(ret == ::ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret); } ASD_STATUS_CHECK(rsInterpolationBySinc( tensorIn, tensorTab, tensorPos, tensorTabIndex, tensorOut, 16, 32, interpLength, stream, workspace)); ret = aclrtSynchronizeStream(stream); CHECK_RET(ret == ::ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret); ret = aclrtMemcpy(outSignal.data(), outSize * sizeof(std::complex<float>), tensorOutDeviceAddr, outSize * sizeof(std::complex<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); for (long long ii = 0; ii < interpLength; ++ii) { std::cout << outSignal[ii] << "\t"; } std::cout << "\nend result" << std::endl; std::cout << "Execute successfully." << std::endl; delete[] tabDate; aclDestroyTensor(tensorIn); aclDestroyTensor(tensorPos); aclDestroyTensor(tensorTab); aclDestroyTensor(tensorTabIndex); aclDestroyTensor(tensorOut); aclrtFree(tensorInDeviceAddr); aclrtFree(tensorTabDeviceAddr); aclrtFree(tensorPosDeviceAddr); aclrtFree(tensorTabIndexDeviceAddr); aclrtFree(tensorOutDeviceAddr); if (workspaceSize > 0) { aclrtFree(workspace); } aclrtDestroyStream(stream); aclrtResetDevice(deviceId); aclFinalize(); return 0; }
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