rsInterpolationBySinc【免费下载链接】sip本项目是CANN提供的一款高效、可靠的高性能信号处理算子加速库基于华为Ascend AI处理器专门为信号处理领域而设计。项目地址: https://gitcode.com/cann/sip产品支持情况产品是否支持Atlas 200I/500 A2 推理产品×Atlas 推理系列产品×Atlas 训练系列产品×Atlas A3 训练系列产品/Atlas A3 推理系列产品√Atlas A2 训练系列产品/Atlas A2 推理系列产品√Ascend 950PR/Ascend 950DT×功能说明接口功能rsInterpolationBySincGetWorkspaceSize计算rsInterpolationBySinc算子所需的workspace大小。rsInterpolationBySinc实现带batch的一维复数向量插值计算返回结果和插值坐标同样形状大小。计算公式 $$ x(d)\sum _{n0}^{N-1}x[n]sinc(d-n) $$ 其中,n为实信号索引x[n]为原实信号序列sinc(d-n)是插值系数在本算子中需要作为参数传入。示例输入“inputTensor”为[ 1 i , 2 i ]输入“sincTab”为intp_num 2 quant_num 2[ [ 1 , 0 ], [ 0.5 , 0.5 ], [ 0 , 1 ] ]原始“pos”为[ 0.2 , 1.6 ]转为输入“posFloor”为 floor(Pos)[ 0 , 1 ]转为输入“posToTabIndex”为round((Pos -posFloor)quant_num)[ 0 , 1 ]其中tab大小为23。由于pos[0] 0.2取inputTensor[0]及后面一个元素inputTensor[1]共2个元素与sincTab[posToTabIndex[0]]进行向量点乘得到outputTensor[0]依次计算后续元素。pos[0] 0.2 → outputTensor[0] [1 i , 2 i] · [ 1 , 0 ] 1 ipos[1] 1.6 → outputTensor[1] [2 i , 2 i] · [ 0.5 , 0.5 ] 2 i调用“rsInterpolationBySinc”算子后输出“outputTensor”为[ 1 i , 2 i ]函数原型若需使用“rsInterpolationBySinc”算子需先调用“rsInterpolationBySincGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器再调用“rsInterpolationBySinc”接口执行计算。AspbStatus rsInterpolationBySincGetWorkspaceSize( const aclTensor * inputTensor, const aclTensor * sincTab, const aclTensor * posFloor, const aclTensor * posToTabIndex, aclTensor * outputTensor, int interpNum, int quantNum, int length, size_t * workspaceSize)AspbStatus rsInterpolationBySinc( const aclTensor * inputTensor, const aclTensor * sincTab, const aclTensor * posFloor, const aclTensor * posToTabIndex, aclTensor * outputTensor, int interpNum, int quantNum, int interpLength, void * stream, void * workSpace nullptr)rsInterpolationBySincGetWorkspaceSize参数说明参数名输入/输出描述inputTensoraclTensor *输入表示原始信号。支持的数据类型为COMPLEX64。数据格式支持ND。shape为[batch, signalLength]。sincTabaclTensor *输入表示插值系数矩阵。支持的数据类型为FLOAT32。数据格式支持ND。shape为[ 4, ((quantNum 1) * 2) * (interpNum * 2 8)]。posFlooraclTensor *输入表示插值点坐标向下取整后的值。支持的数据类型为INT32。数据格式支持ND。shape为[batch, length]。posToTabIndexaclTensor *输入插值点坐标通过round((Pos -posFloor) *quantNum)计算出对应插值系数矩阵的行号。支持的数据类型为INT16_T。数据格式支持ND。shape为[batch, length]。outputTensoraclTensor *输出插值结果。支持的数据类型为COMPLEX64。数据格式支持ND。shape为[batch, length]。interpNumint输入插值点数。quantNumint输入量化点数。lengthint输入插值长度。workspaceSizesize_t *输出workspace的地址。返回值返回状态码具体参见SiP返回码。rsInterpolationBySinc参数说明参数名输入/输出描述inputTensoraclTensor *输入表示原始信号。支持的数据类型为COMPLEX64。数据格式支持ND。shape为[batch, signalLength]。sincTabaclTensor *输入表示插值系数矩阵。支持的数据类型为FLOAT32。数据格式支持ND。shape为[ 4, ((quantNum 1) * 2) * (interpNum * 2 8)]。posFlooraclTensor *输入表示插值点坐标向下取整后的值。支持的数据类型为INT32。数据格式支持ND。shape为[batch, length]。posToTabIndexaclTensor *输入插值点坐标通过round((Pos -posFloor) *quantNum)计算出对应插值系数矩阵的行号。支持的数据类型为INT16_T。数据格式支持ND。shape为[batch, length]。outputTensoraclTensor *输出插值结果。支持的数据类型为COMPLEX64。数据格式支持ND。shape为[batch, length]。interpNumint输入插值点数。quantNumint输入量化点数。lengthint输入插值长度。streamvoid *输入npu执行流。workspaceSizevoid *输入workspace的地址。返回值返回状态码具体参见SiP返回码。约束说明rsInterpolationBySinc输入的元素个数理论支持[13.93e09]。算子实际计算时不支持ND高维度运算不支持维度≥3的运算。interpNum只支持偶数通常使用 [8 12 16] 当前版本最大支持16。quantNum为2的幂最大32。inputTensor、posFloor、posToTabIndex第0维是相同的batch数outputTensors长度和posFloor、posToTabIndex一致。sincTab为了将复数点乘转化为实数点乘以及更亲和NPU需要进行预处理需要扩充成[ ((quantNum1)2) * (interpNum28) * 4] 具体算法参考“调用示例”中的预处理内容。调用示例示例代码如下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。rsInterpolationBySinc算子调用说明\调用示例中会进行sincTab预处理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此时虚数矩阵会被扩充成 ((quantNum1)2)(interpNum*2)。[系数1,0][ 0,系数1]\为了亲和NPU32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其中“genTab”函数用于生成如下类似系数矩阵 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,w15,0,0,0,0,0,0,0,00,0,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,w15,0,0,0,0,0,00,0,0,0,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,w15,0,0,0,00,0,0,0,0,0,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,w15,0,0rsInterpolationBySinc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::vectorint64_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::vectorT hostData, const std::vectorint64_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::vectorint64_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::vectorfloat tab(tabDate, tabDate tabSize); std::vectorcomplexfloat inSignal; inSignal.reserve(inSize); for (long long ii 0; ii signalLength; ii) { inSignal[ii] complexfloat(ii, ii); } std::vectorint32_t intpPos; intpPos.reserve(posSize); for (long long ii 0; ii interpLength; ii) { intpPos[ii] ii; } std::vectorint16_t tabIndex; tabIndex.reserve(tabIndexSize); for (long long ii 0; ii interpLength; ii) { tabIndex[ii] ii % 33; } std::vectorcomplexfloat outSignal; outSignal.reserve(outSize); for (long long ii 0; ii interpLength; ii) { outSignal[ii] complexfloat(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_castint64_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::complexfloat), tensorOutDeviceAddr, outSize * sizeof(std::complexfloat), 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; }【免费下载链接】sip本项目是CANN提供的一款高效、可靠的高性能信号处理算子加速库基于华为Ascend AI处理器专门为信号处理领域而设计。项目地址: https://gitcode.com/cann/sip创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考