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MulAddDst

功能说明

按元素将src0Local和src1Local相乘并和dstLocal相加,将最终结果存放进dstLocal中。计算公式如下,其中PAR表示矢量计算单元一个迭代能够处理的元素个数:

函数原型

  • tensor前n个数据计算

    template <typename T, template U> __aicore__ inline void MulAddDst(const LocalTensor<T>& dstLocal, const LocalTensor<U>& src0Local, const LocalTensor<U>& src1Local, const int32_t& calCount);

  • tensor高维切分计算
    • mask逐bit模式

      template <typename T, template U, bool isSetMask = true> __aicore__ inline void MulAddDst(const LocalTensor<T>& dstLocal, const LocalTensor<U>& src0Local, const LocalTensor<U>& src1Local, uint64_t mask[2], const uint8_t repeatTimes, const BinaryRepeatParams& repeatParams)

    • mask连续模式

      template <typename T, template U, bool isSetMask = true> __aicore__ inline void MulAddDst(const LocalTensor<T>& dstLocal, const LocalTensor<U>& src0Local, const LocalTensor<U>& src1Local, uint64_t mask, const uint8_t repeatTimes, const BinaryRepeatParams& repeatParams);

参数说明

表1 模板参数说明

参数名

描述

T

目的操作数数据类型。

U

源操作数数据类型。

isSetMask

是否在接口内部设置mask。

  • true,表示在接口内部设置mask。
  • false,表示在接口外部设置mask,开发者需要使用SetVectorMask接口设置mask值。这种模式下,本接口入参中的mask值必须设置为MASK_PLACEHOLDER。
表2 参数说明

参数名

输入/输出

描述

dstLocal

输出

目的操作数。

类型为LocalTensor,支持的TPosition为VECIN/VECCALC/VECOUT。

Atlas A2训练系列产品/Atlas 800I A2推理产品,支持的数据类型为:half/float

Atlas推理系列产品(Ascend 310P处理器)AI Core,支持的数据类型为:half/float

src0Localsrc1Local

输入

源操作数。源操作数数据类型和目的操作数数据类型可以不一致。

类型为LocalTensor,支持的TPosition为VECIN/VECCALC/VECOUT。

Atlas A2训练系列产品/Atlas 800I A2推理产品,支持的数据类型为:half/float

Atlas推理系列产品(Ascend 310P处理器)AI Core,支持的数据类型为:half/float

calCount

输入

输入数据元素个数。

mask

输入

mask用于控制每次迭代内参与计算的元素。

  • 连续模式:表示前面连续的多少个元素参与计算。数据类型为uint64。取值范围和操作数的数据类型有关,数据类型不同,每次迭代内能够处理的元素个数最大值不同。当操作数为16位时,mask∈[1, 128];当操作数为32位时,mask∈[1, 64]。
  • 逐bit模式:可以按位控制哪些元素参与计算,bit位的值为1表示参与计算,0表示不参与。参数类型为长度为2的uint64_t类型数组。

    例如,mask=[8, 0],8=0b1000,表示仅第4个元素参与计算。

    参数取值范围和操作数的数据类型有关,数据类型不同,每次迭代内能够处理的元素个数最大值不同。当操作数为16位时,mask[0]、mask[1]∈[0, 264-1]并且不同时为0;当操作数为32位时,mask[1]为0,mask[0]∈(0, 264-1];当操作数为64位时,mask[1]为0,mask[0]∈(0, 232-1]。

repeatTimes

输入

重复迭代次数。矢量计算单元,每次读取连续的256 Bytes数据进行计算,为完成对输入数据的处理,必须通过多次迭代(repeat)才能完成所有数据的读取与计算。repeatTimes表示迭代的次数。

关于该参数的具体描述请参考通用参数说明

repeatParams

输入

控制操作数地址步长的数据结构。结构体内包含操作数相邻迭代间相同datablock的地址步长,操作数同一迭代内不同datablock的地址步长等参数。

该数据结构的定义请参考BinaryRepeatParams

相邻迭代间的地址步长参数说明请参考Repeat stride(相邻迭代间相同datablock的地址步长);同一迭代内datablock的地址步长参数说明请参考Block stride(同一迭代内不同datablock的地址步长)

返回值

支持的型号

Atlas A2训练系列产品/Atlas 800I A2推理产品

Atlas推理系列产品(Ascend 310P处理器)AI Core

约束说明

  • 使用高维切分计算接口时,节省地址空间,开发者可以定义一个Tensor,供源操作数与目的操作数同时使用(即地址重叠),相关约束如下:
    • 单次迭代内,要求源操作数和目的操作数之间100%重叠,不支持部分重叠。
    • 多次迭代间,第N次目的操作数是第N+1次源操作数的情况下,是不支持地址重叠的,因为第N+1次依赖第N次的结果。
  • 该接口支持的精度组合如下:
    • half精度组合:src0Local数据类型=half;src1Local数据类型=half;dstLocal数据类型=half;PAR=128
    • float精度组合:src0Local数据类型=float;src1Local数据类型=float;dstLocal数据类型=float;PAR=64
    • mix精度组合:src0Local数据类型=half;src1Local数据类型=half;dstLocal数据类型=float;PAR=64
  • mix精度组合下,源操作数和目的操作数无法100%重叠,故不支持地址重叠。

调用示例

本样例中只展示Compute流程中的部分代码。如果您需要运行样例代码,请将该代码段拷贝并替换双目指令样例模板更多样例中的Compute函数即可。

  • 高维切分计算接口样例-mask连续模式(mix精度组合)
    uint64_t mask = 64;
    // repeatTimes = 4, 一次迭代计算64个数, 共计算256个数
    // dstBlkStride, src0BlkStride, src1BlkStride = 1, 单次迭代内数据连续读取和写入
    // dstRepStride = 8, src0RepStride, src1RepStride = 4, 相邻迭代间数据连续读取和写入
    MulAddDst(dstLocal, src0Local, src1Local, 64, 4, { 1, 1, 1, 8, 4, 4 });
  • 高维切分计算接口样例-mask逐bit模式(mix精度组合)
    uint64_t mask[2] = { UINT64_MAX, 0 };
    // repeatTimes = 4, 一次迭代计算64个数, 共计算256个数
    // dstBlkStride, src0BlkStride, src1BlkStride = 1, 单次迭代内数据连续读取和写入
    // dstRepStride = 8, src0RepStride, src1RepStride = 4, 相邻迭代间数据连续读取和写入
    MulAddDst(dstLocal, src0Local, src1Local, mask, 4, { 1, 1, 1, 8, 4, 4 });
  • tensor前n个数据计算样例(mix精度组合)
    MulAddDst(dstLocal, src0Local, src1Local, 256);
结果示例如下:
输入数据(src0Local): 
[-83.     58.2   -14.28  -43.12   20.72  -79.9    54.16   31.56   -1.464
  68.25  -28.31  -93.5    -4.2   -46.56  -22.23   78.5   -69.56  -37.03
 -53.12   58.28  -71.56  -34.44   85.94   96.3    66.06   99.94  -45.94
   8.75  -93.9    35.56   82.56  -70.8   -68.75  -35.4    95.3   -49.1
 -56.34   86.75   90.25   24.17   79.06  -49.66  -95.3    -6.965 -63.72
 -33.16  -15.56  -43.28   51.28   40.1    83.25   49.72   55.47  -53.7
  17.55  -36.06   63.     59.16  -66.8    -9.01   25.56   44.28   22.12
 -33.84  -31.9   -74.2    79.94  -34.94    1.119  18.45  -92.75  -83.25
  42.66  -77.6    33.28    0.709 -19.3    44.44   45.28  -33.4   -55.94
 -42.22  -37.72   39.4    87.25   23.19   34.16   51.3   -22.16   15.234
  59.    -20.45  -63.9    41.84  -14.63  -80.94   47.8   -36.84    8.47
 -60.66  -26.06  -42.78   30.5   -91.3    55.84  -85.44  -99.44   68.2
 -71.7    27.45  -11.48  -48.03   71.     71.5   -59.2    14.67   79.25
  32.7   -54.22    6.17  -69.94  -49.22   87.7   -61.53   36.25  -57.84
 -81.75  -24.84  -35.    -62.44  -47.22   19.95   21.16  -31.56   13.38
  72.4   -64.06  -89.75  -28.17   34.4   -68.06  -46.94   16.06   65.56
   3.16  -59.88  -32.97   30.69   89.5    16.66   25.05   -1.988   5.27
 -23.14  -26.89  -24.72    1.427 -14.46   81.9   -59.94   68.7   -83.2
 -75.44   88.6    27.62  -58.06  -36.1   -49.53   27.73   89.5   -51.5
  90.     67.94  -70.8    24.2   -75.8   -96.75  -22.66   33.03    6.293
 -87.5    36.56   36.06  -76.8     1.786  82.9    87.6   -63.94   -4.51
 -89.06  -56.06   75.2   -31.89   27.44   35.22  -27.19   37.53   96.94
 -83.25  -49.6    31.78  -50.25   65.2    69.9    63.03   53.    -70.1
 -57.22  -11.99  -23.14   44.28  -77.3    77.25   10.805  16.3   -96.6
 -94.9    34.1   -40.25  -99.7    -6.156  44.97   82.7    51.1   -53.28
  85.44  -80.94  -47.    -53.47  -35.22   76.75  -28.38   26.48  -67.06
  34.28  -54.6    21.52  -38.9    79.75   51.7   -39.44   48.56  -91.7
 -44.06   92.9    11.79    8.98   -5.074  12.375 -24.77  -27.31   76.2
  39.8    -5.46   25.17   47.   ]
输入数据(src1Local): 
[-57.97    43.5      8.08    72.4    -81.44   -52.      69.1    -84.25
  31.12    34.34    74.75    83.56   -83.      80.1     42.84   -31.6
  88.56    47.34    18.89   -95.25    16.88   -85.75    76.75   -17.19
  23.39    92.56    22.81    77.94    38.62   -55.8     38.22   -88.6
 -99.4    -66.75    90.44    80.56    12.78   -12.6    -68.4      2.816
  27.45   -60.88    70.      61.78   -90.56   -99.25    38.25   -14.49
 -35.88    38.1     13.      29.22   -57.06   -44.7      6.535  -44.6
 -76.3     91.7     36.66    83.9     66.     -81.25   -50.06    68.
   2.705  -51.72    66.9     49.03    15.76     9.37    33.2     99.56
 -20.55    83.3    -57.1     37.06    68.94   -91.9    -46.06   -92.7
  64.4      8.164    8.98    10.76   -75.6     26.94    46.8     62.
   8.734  -69.25   -70.2    -59.      67.25    87.6     48.72    60.16
  19.39    48.62    21.64    25.06     1.013  -36.6    -46.28   -29.14
  67.44    56.7     32.03   -28.81   -94.44    49.6      0.583  -84.4
 -51.53   -43.      66.     -68.      77.44   -50.16   -90.4    -46.22
  90.25    88.      79.25   -40.84   -71.7    -27.03    19.53    85.44
  45.06    60.72    19.22   -28.95   -47.72    97.8    -51.6     31.42
  31.75   -21.84   -71.4     77.9     43.12    35.66   -50.84   -52.
 -48.84   -53.97   -59.56    31.2    -64.3    -10.47    86.25   -84.44
 -56.4    -63.03   -99.9     54.44    40.72    74.94     8.305   18.52
 -47.34   -74.06    79.1     92.44    84.94   -98.7    -41.06   -80.2
 -71.06    89.06    96.2    -19.83   -51.03   -92.      82.25   -75.75
  58.66    22.72   -89.06   -83.06   -73.5     18.75    -0.939  -96.4
  50.12   -73.9    -56.97    52.34   -95.56    11.02   -46.3    -52.2
  -8.46    80.56    77.     -51.72    38.8    -66.44   -69.     -30.33
 -53.3      5.406   74.8     52.25   -35.88    92.5     51.38    40.47
  43.94   -29.05    89.7    -74.5    -83.5     81.75   -56.6    -13.625
  86.9     -4.58   -67.5     -6.67   -59.53   -30.4    -91.75   -84.3
 -66.6    -28.61   -13.79   -70.75   -90.2    -47.94    59.56    84.2
   0.7085 -57.44   -24.94   -11.875  -90.4     54.22   -44.16   -36.34
 -31.64    72.1    -81.25    75.8     93.9    -28.28   -20.53    90.2
 -58.97   -95.7     59.22   -37.8     94.9    -86.7     36.16    26.47  ]
输入数据(dstLocal):
[-97.94773    -61.303955    32.56878    -87.50743    -78.92147
  59.20739     50.336506    49.039738   -76.2525       0.25441223
 -71.73807      6.481831   -55.5052     -51.057415    31.403702
  63.285076    98.1897      86.71727    -50.16466     88.94256
  72.111435     8.4164915   34.524082    73.14016      4.838548
  69.67902    -97.855736    90.358696     9.051491    37.595695
 -66.01661    -97.110634    82.84477     69.46122     25.561102
  47.926853   -10.202202    78.2545      31.339691    12.940468
 -31.499294    -3.351652    62.46355     45.0427     -86.02812
 -43.48385    -62.274956   -36.077827    51.81446     32.47797
  59.10228     68.18655      9.3604145  -76.47674    -50.29268
  94.496346    30.837933   -48.315712   -44.92399    -62.369625
  47.578724    84.84092    -66.64584     88.376434    95.05615
 -92.37309      3.0038757   85.21814     -6.688882    97.74142
  20.733965    -5.62451     69.6166     -64.435455    94.09325
 -63.13334     89.150345   -17.61865     32.776333    27.28345
  31.288876    -9.983517   -46.39662    -37.025536    47.853374
 -30.384796   -79.801544   -11.131944   -36.417023    84.25002
 -74.19904    -86.72338     -6.5878353   26.253004   -28.112898
 -64.88305    -40.56897    -65.849686    22.276798    -3.356709
 -78.41364    -67.26924    -10.346288   -43.172684    10.149812
 -22.575602   -28.780804   -64.24396    -14.579756   -30.369322
 -59.28742    -37.098255    31.078829    29.901808    50.531147
 -88.35735    -45.65366     -6.7495203    6.8026304   56.172153
  -0.8727364    9.618746    89.294815    75.4403      81.63827
 -61.722088   -72.85743      9.296161   -69.17855      2.3497865
  20.234892   -13.279363   -44.531677    55.188084   -45.736256
 -30.018398    27.09971     28.841034    35.764072    21.457811
 -15.206495    94.05271     79.9942     -36.39198     38.40136
   5.2365685  -11.435508    67.15551     87.03286      7.9285994
  78.32062     97.863335   -28.68556    -72.658554   -79.39075
 -82.65206     39.52689    -22.053177    30.602457   -26.158005
  49.83525    -72.24563    -97.10148     54.803936    65.070786
 -57.019573    35.972733     6.694148   -74.88097    -71.13884
 -84.549545   -26.875593    -3.2775877   -8.592472    -5.248627
 -22.2127      98.26377    -51.741936   -69.48398    -47.230175
  92.72371     18.192408   -39.66745     44.556633   -21.733562
  15.191482     5.9535656   41.23602     89.30139    -32.57541
 -47.595608   -50.371124   -87.899666    57.644466    38.85747
  47.65093     49.42874    -32.424126   -22.5012      78.78245
 -70.6598     -87.218544    50.347565    55.945244    -3.4658287
  17.902784   -30.977674    53.424767   -82.00753      2.9060571
  -1.010124   -94.316765    13.186674   -52.089214    58.975357
  48.281635    26.436571   -27.11565     89.21593    -10.962796
  49.347828    21.556795    78.163956    35.06028     10.803711
  53.231297   -44.78757     -0.6473386   26.717777    63.757347
  -4.90904     21.724916    37.443634   -89.250656    62.98874
  72.13095    -12.19138     84.16487     71.54008    -73.41178
 -97.612564    39.947853    -1.3887504   -5.6196795  -54.509125
 -28.877354    26.259935    42.28702    -38.848114   -76.46558
 -91.69401     71.27111     89.36143    -65.70425    -31.810083
  82.811226  ]
输出数据(dstLocal): 
[ 4.71345850e+03  2.46985229e+03 -8.27969437e+01 -3.20867920e+03
 -1.76620471e+03  4.21270752e+03  3.79388721e+03 -2.61010083e+03
 -1.21815369e+02  2.34421533e+03 -2.18809741e+03 -7.80661182e+03
  2.93029968e+02 -3.78187769e+03 -9.21200317e+02 -2.41682422e+03
 -6.06243945e+03 -1.66648096e+03 -1.05372913e+03 -5.46234668e+03
 -1.13550574e+03  2.96143213e+03  6.63022705e+03 -1.58223096e+03
  1.55008167e+03  9.32014355e+03 -1.14580493e+03  7.72311829e+02
 -3.61687036e+03 -1.94723633e+03  3.08941895e+03  6.17864697e+03
  6.91487598e+03  2.43282837e+03  8.64538574e+03 -3.90718848e+03
 -7.30345764e+02 -1.01493103e+03 -6.13950391e+03  8.10182877e+01
  2.13901343e+03  3.01947266e+03 -6.60941162e+03 -3.85254059e+02
  5.68450098e+03  3.24727393e+03 -6.57540588e+02  5.91162170e+02
 -1.78790039e+03  1.55979932e+03  1.14135229e+03  1.52090625e+03
 -3.15582520e+03  2.32268335e+03  6.43788910e+01  1.70265845e+03
 -4.77684961e+03  5.37557275e+03 -2.49401978e+03 -8.17899902e+02
  1.73470374e+03 -3.51301074e+03 -1.17427869e+03 -2.21299854e+03
  8.74725342e+00  3.74451172e+03  5.34882422e+03 -1.62781116e+03
  1.09463263e+01  2.70595306e+02 -3.05740674e+03 -8.29420215e+03
 -8.06836060e+02 -6.53156836e+03 -1.80605811e+03 -3.68566055e+01
 -1.24112793e+03 -4.10031396e+03 -2.05299121e+03  3.12362524e+03
 -3.56968774e+03 -3.54660034e+02 -3.84981323e+02  3.86899506e+02
 -6.55042773e+03  5.94228516e+02  1.51913794e+03  3.17024316e+03
 -2.29938019e+02 -9.70730469e+02 -4.21526172e+03  1.12001099e+03
 -4.30428320e+03  3.69281152e+03 -7.41005249e+02 -4.93377930e+03
  8.86545288e+02 -1.85737708e+03  2.05545837e+02 -1.52355396e+03
 -1.04807014e+02  1.49825708e+03 -1.42192444e+03  2.61773071e+03
  3.77611279e+03 -4.86581396e+03 -3.21388818e+03 -2.02889624e+03
  6.75540869e+03  1.33113416e+03 -6.59783478e+01  4.01553857e+03
 -3.62763989e+03 -3.04459814e+03 -3.85584375e+03 -1.08604480e+03
  6.09126807e+03 -1.64623193e+03  4.90682227e+03 -2.29084198e+02
 -6.31273193e+03 -4.32163135e+03  7.03852930e+03  2.58860718e+03
 -2.51703369e+03  1.50186682e+03 -1.66953711e+03 -2.11329175e+03
 -1.64636609e+03 -3.78877710e+03 -8.87250488e+02 -5.90984680e+02
 -1.05408154e+03 -3.03201904e+03 -7.36205750e+02  2.24413989e+03
 -2.00688464e+03  1.98931763e+03  2.04653162e+03  2.70084448e+03
 -2.95040186e+03 -1.57956250e+03 -7.36683533e+02 -3.44564209e+03
 -1.15952522e+02  3.23661548e+03  1.95226562e+03  1.02470142e+03
 -5.66893604e+03 -1.66441513e+02  2.23861353e+03  2.65748840e+02
 -3.25920044e+02  1.38592395e+03  2.60631055e+03 -1.42827905e+03
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