WholeReduceMax

Applicability

Product	Supported
Atlas A3 training products / Atlas A3 inference products	√
Atlas A2 training products / Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	√
Atlas inference product 's AI Core	√
Atlas inference product 's Vector Core	x
Atlas training products	√

Function

Computes the maximum value of all data and its index in each repeat. The returned index value is the internal index of each repeat.

Prototype

Bitwise mask mode

        
             template <typename T, bool isSetMask = true>
__aicore__ inline void WholeReduceMax(const LocalTensor<T>& dst, const LocalTensor<T>& src, const uint64_t mask[], const int32_t repeatTime, const int32_t dstRepStride, const int32_t srcBlkStride, const int32_t srcRepStride, ReduceOrder order = ReduceOrder::ORDER_VALUE_INDEX)

Contiguous mask mode

        
             template <typename T, bool isSetMask = true>
__aicore__ inline void WholeReduceMax(const LocalTensor<T>& dst, const LocalTensor<T>& src, const int32_t mask, const int32_t repeatTime, const int32_t dstRepStride, const int32_t srcBlkStride, const int32_t srcRepStride, ReduceOrder order = ReduceOrder::ORDER_VALUE_INDEX)

Parameters

**Table 1** Template parameters
Parameter	Description
T	Operand data type. For the Atlas A3 training products / Atlas A3 inference products , the supported data types are half and float. For the Atlas A2 training products / Atlas A2 inference products , the supported data types are half and float. For Atlas 200I/500 A2 inference products , the supported data types are half and float. For the Atlas inference product 's AI Core, the supported data types are half and float. For the Atlas training products , the supported data type is half.
isSetMask	Indicates whether to set mask inside the API. true: sets mask inside the API. false: sets mask outside the API. Developers need to use the SetVectorMask API to set the mask value. In this mode, the mask value in the input parameter of this API must be set to the placeholder MASK_PLACEHOLDER.

**Table 2** Parameters
Parameter	Input/Output	Meaning
dst	Output	Destination operand. The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT. The start address of the LocalTensor must be 4-byte aligned (for data of the half type) or 8-byte aligned (for data of the float type).
src	Input	Source operand. The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT. The start address of the LocalTensor must be 32-byte aligned. The data type of the source operand must be the same as that of the destination operand.
mask/mask[]	Input	mask is used to control the elements that participate in computation in each iteration. Bitwise mode: controls which elements are involved in computation bit by bit. A bit value of 1 means the corresponding element participates in computation, while 0 means it does not. The mask value is an array. The array length and the value range of the array elements are related to the operand data type. When the operand is 16-bit, the array length is 2, mask[0] and mask[1] ∈ [0, 2⁶⁴ -1] and cannot be 0 at the same time. When the operand is 32-bit, the array length is 1 and mask[0] ∈ (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1 and mask[0] ∈ (0, 2³² – 1]. For example, if mask = [0, 8] and 8 = 0b1000, only the fourth element participates in computation. Contiguous mode: indicates the number of contiguous elements that participate in computation. The value range is related to the operand data type. The maximum number of elements that can be processed in each repeat varies according to the data type. When the operand is 16-bit, mask ∈ [1, 128]. When the operand is 32-bit, mask ∈ [1, 64]. When the operand is 64-bit, mask ∈ [1, 32].
repeatTime	Input	Number of iteration repeats. The value range is [0, 255]. For details about this parameter, see High-dimensional Sharding APIs.
dstRepStride	Input	Address stride between adjacent iterations of the destination operand. The unit is the length after reduction of a repeat. When the index and maximum/minimum value are returned, the unit is twice the length of the data type of dst. For example, when dst is of the half type, the unit is 4 bytes. When only the maximum/minimum value is returned, the unit is the length of the data type of dst. When only the index is returned, the unit is the length of the data type of uint32_t. Note that this parameter cannot be set to 0 for the Atlas training products .
srcBlkStride	Input	Address stride of data blocks in a single iteration. For details, see dataBlockStride.
srcRepStride	Input	Address stride between adjacent iterations of the source operand, that is, the number of data blocks skipped of the source operand in each iteration.
order	Input	Storage order of the value and index in dst and the result return behavior. The value is of the ReduceOrder type and defaults to ORDER_VALUE_INDEX. The values are as follows: ORDER_VALUE_INDEX: indicates that the value is in the lower half. The storage sequence of returns is [value, index]. ORDER_INDEX_VALUE: indicates that the index is in the lower half. The storage sequence of returns is [index, value]. ORDER_ONLY_VALUE: indicates that only the maximum value is returned. The storage sequence of returns is [value]. ORDER_ONLY_INDEX: indicates that only the index of the maximum value is returned. The storage sequence of returns is [value]. For the Atlas A3 training products / Atlas A3 inference products , ORDER_VALUE_INDEX, ORDER_INDEX_VALUE, ORDER_ONLY_VALUE, and ORDER_ONLY_INDEX are supported. For the Atlas A2 training products / Atlas A2 inference products , ORDER_VALUE_INDEX, ORDER_INDEX_VALUE, ORDER_ONLY_VALUE, and ORDER_ONLY_INDEX are supported. For the Atlas 200I/500 A2 inference products , ORDER_VALUE_INDEX and ORDER_ONLY_VALUE are supported. For the Atlas inference product 's AI Core, ORDER_VALUE_INDEX and ORDER_INDEX_VALUE are supported. For the Atlas training products , ORDER_VALUE_INDEX is supported.

Returns

None

Restrictions

For details about the operand address alignment requirements, see General Address Alignment Restrictions.
For details about the constraints on operand address overlapping, see General Address Overlapping Restrictions.

The storage order in dst is determined by the order parameter and defaults to maximum/minimum value followed by its index. In the returned result, the index is stored using the data type of dst. For example, if dst is of type half, the index is stored as half and must be converted to an integer using reinterpret_cast when it is read. If the input type is half, reinterpret_cast<uint16_t*> is required. If the input type is float, reinterpret_cast<uint32_t*> is required. In the complete example, the first two computation results are [9.980e-01 5.364e-06], the reinterpret_cast method is called to convert 5.364e-06 to the index value 90. For Atlas A2 training products / Atlas A2 inference products and Atlas A3 training products / Atlas A3 inference products , when ORDER_ONLY_INDEX (only the index of the maximum or minimum value is returned) is used, reinterpret_cast<uint32_t*> must be used to read the index.
Proper use of reduction instructions in different scenarios can improve performance. For details, see Selecting Low-Latency Instructions to Optimize Reduction Operation Performance. For details about examples, see ReduceCustom.

Example

Example of high-dimensional tensor sharding computation (contiguous mask mode)

        
             // Both dstLocal and srcLocal are of the half type. For srcLocal, the computation data is of size 512 and is continuously arranged. Its compute result is also continuously arranged. It uses the high-dimensional tensor sharding computation API. mask is set to 128, indicating that all elements are involved in the computation.
// Based on the preceding information, repeatTime is 4, dstRepStride is 1, srcBlkStride is 1, and srcRepStride is 8.
// To obtain the maximum value and index that are stored in the format of [value, index], you can use the default order. The following is an example:
AscendC::WholeReduceMax<half>(dstLocal, srcLocal, 128, 4, 1, 1, 8);
// To obtain the maximum value and index that are stored in the format of [index, value], you can use the following example API:
AscendC::WholeReduceMax<half>(dstLocal, srcLocal, 128, 4, 1, 1, 8, AscendC::ReduceOrder::ORDER_INDEX_VALUE);

Example of high-dimensional tensor sharding computation (bitwise mask mode)

        
             // Both dstLocal and srcLocal are of the half type. For srcLocal, the computation data is of size 512 and is continuously arranged. Its compute result is also continuously arranged. It uses the high-dimensional tensor sharding computation API. mask is set to 128, indicating that all elements are involved in the computation.
// Based on the preceding information, repeatTime is 4, dstRepStride is 1, srcBlkStride is 1, and srcRepStride is 8.
// To obtain the maximum value and index that are stored in the format of [value, index], you can use the default order. The following is an example:
uint64_t mask[2] = { 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF };
AscendC::WholeReduceMax<half>(dstLocal, srcLocal, mask, 4, 1, 1, 8);

Complete example:

        
         
           
           
             #include "kernel_operator.h"
class KernelReduce {
public:
    __aicore__ inline KernelReduce() {}
    __aicore__ inline void Init(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
    {
        srcGlobal.SetGlobalBuffer((__gm__ half*)src);
        dstGlobal.SetGlobalBuffer((__gm__ half*)dstGm);
        repeat = srcDataSize / mask;
        pipe.InitBuffer(inQueueSrc, 1, srcDataSize * sizeof(half));
        pipe.InitBuffer(outQueueDst, 1, dstDataSize * sizeof(half));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
    }
private:
    __aicore__ inline void CopyIn()
    {
        AscendC::LocalTensor<half> srcLocal = inQueueSrc.AllocTensor<half>();
        AscendC::DataCopy(srcLocal, srcGlobal, srcDataSize);
        inQueueSrc.EnQue(srcLocal);
    }
    __aicore__ inline void Compute()
    {
        AscendC::LocalTensor<half> srcLocal = inQueueSrc.DeQue<half>();
        AscendC::LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();
        AscendC::WholeReduceMax<half> (dstLocal, srcLocal, mask, repeat, 1, 1, 8); // Use the default order. ReduceOrder::ORDER_VALUE_INDEX
        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc.FreeTensor(srcLocal);
    }
    __aicore__ inline void CopyOut()
    {
        AscendC::LocalTensor<half> dstLocal = outQueueDst.DeQue<half>();
        AscendC::DataCopy(dstGlobal, dstLocal, dstDataSize);
        outQueueDst.FreeTensor(dstLocal);
    }
private:
    AscendC::TPipe pipe;
    AscendC::TQue<AscendC::TPosition::VECIN, 1> inQueueSrc;
    AscendC::TQue<AscendC::TPosition::VECOUT, 1> outQueueDst;
    AscendC::GlobalTensor<half> srcGlobal, dstGlobal;
    int srcDataSize = 1024;
    int dstDataSize = 16;
    int mask = 128;
    int repeat = 0;
};
extern "C" __global__ __aicore__ void reduce_kernel(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
{
    KernelReduce op;
    op.Init(src, dstGm);
    op.Process();
}

            

          

        
       

The following is an example:

Input (src_gm):
[0.00787  0.8516   0.01558  0.152    0.887    0.2532   0.2272   0.1295
 0.7207   0.628    0.5522   0.991    0.3164   0.961    0.526    0.5513
 0.03973  0.3293   0.809    0.562    0.915    0.56     0.3464   0.3438
 0.6094   0.1201   0.8384   0.848    0.004436 0.4263   0.01917  0.753
 0.9126   0.2307   0.1066   0.644    0.8657   0.7085   0.7915   0.1707
 0.3806   0.957    0.0483   0.858    0.10675  0.21     0.03345  0.55
 0.3757   0.3281   0.927    0.09406  0.6445   0.985    0.405    0.09393
 0.773    0.7227   0.03714  0.595    0.889    0.0948   0.4202   0.2747
 0.5894   0.3022   0.894    0.675    0.6016   0.938    0.585    0.5244
 0.8643   0.888    0.794    0.636    0.976    0.148    0.7427   0.1742
 0.32     0.0649   0.2954   0.2018   0.833    0.0976   0.4048   0.2861
 0.8765   0.722    0.998    0.03041  0.005512 0.9087   0.9873   0.1436
 0.4812   0.1901   0.78     0.6934   0.2317   0.3782   0.8613   0.808
 0.06885  0.3584   0.5684   0.541    0.5415   0.3096   0.5957   0.9043
 0.7964   0.501    0.4324   0.7544   0.687    0.8447   0.526    0.548
 0.926    0.9106   0.1616   0.183    0.6704   0.642    0.4783   0.1797
 0.2078   0.59     0.4866   0.4683   0.649    0.7266   0.4976   0.8364
 0.6245   0.07385  0.0786   0.586    0.7827   0.3298   0.9497   0.1617
 0.4375   0.3572   0.2896   0.6465   0.1156   0.4905   0.2617   0.8267
 0.2054   0.1415   0.2993   0.8374   0.754    0.942    0.6416   0.1222
 0.1465   0.3335   0.3577   0.6484   0.614    0.5825   0.6807   0.9297
 0.694    0.759    0.908    0.9126   0.4731   0.963    0.3271   0.724
 0.4077   0.335    0.672    0.4219   0.1818   0.843    0.2708   0.0816
 0.457    0.3481   0.67     0.6895   0.6924   0.191    0.2013   0.2484
 0.8833   0.9146   0.4102   0.1063   0.6685   0.804    0.6606   0.2491
 0.34     0.3281   0.823    0.603    0.521    0.6797   0.401    0.5
 0.03683  0.04758  0.507    0.667    0.9014   0.263    0.2477   0.0179
 0.8735   0.007023 0.545    0.758    0.3508   0.6333   0.9375   0.5903
 0.2732   0.0847   0.489    0.196    0.5557   0.403    0.9204   0.3655
 0.5083   0.7515   0.3347   0.6914   0.2185   0.2458   0.5537   0.3457
 0.4878   0.869    0.908    0.0877   0.295    0.9      0.9307   0.05545
 0.4639   0.4001   0.8433   0.4883   0.916    0.7026   0.5063   0.05164
 0.936    0.844    0.2086   0.625    0.0197   0.4312   0.3677   0.983
 0.625    0.004665 0.2479   0.3093   0.9214   0.003672 0.7915   0.921
 0.331    0.01127  0.703    0.6416   0.4053   0.53     0.9688   0.10297
 0.5547   0.07367  0.2305   0.02821  0.8115   0.4202   0.0561   0.0917
 0.04828  0.536    0.0905   0.328    0.8413   0.3696   0.982    0.3733
 0.436    0.753    0.1937   0.8706   0.991    0.273    0.763    0.418
 0.4446   0.513    0.6724   0.1179   0.921    0.756    0.7144   0.6196
 0.9634   0.562    0.3088   0.864    0.709    0.6797   0.2114   0.534
 0.5225   0.1852   0.038    0.5454   0.8823   0.849    0.608    0.7734
 0.7446   0.7236   0.1903   0.1031   0.497    0.57     0.172    0.1907
 0.6333   0.641    0.681    0.2323   0.1007   0.4094   0.3655   0.4248
 0.08044  0.1483   0.08716  0.354    0.128    0.3933   0.775    0.215
 0.728    0.909    0.4204   0.618    0.2517   0.9106   0.3647   0.5977
 0.3445   0.315    0.488    0.99     0.9443   0.6196   0.9287   0.088
 0.9946   0.796    0.7515   0.1912   0.4312   0.7974   0.735    0.01536
 0.7456   0.643    0.484    0.218    0.9272   0.1703   0.1885   0.1982
 0.754    0.902    0.848    0.05832  0.4138   0.6885   0.3853   0.3499
 0.639    0.5786   0.6353   0.5664   0.02621  0.56     0.532    0.08246
 0.733    0.1334   0.0728   0.7817   0.5273   0.126    0.179    0.7334
 0.1565   0.457    0.4807   0.6987   0.5845   0.6206   0.902    0.9277
 0.501    0.6763   0.3418   0.7925   0.07556  0.0929   0.9014   0.3145
 0.04907  0.7188   0.958    0.7275   0.1963   0.1742   0.785    0.518
 0.61     0.1112   0.481    0.10583  0.198    0.181    0.3271   0.2773
 0.2391   0.5625   0.621    0.173    0.05936  0.5654   0.838    0.865
 0.01523  0.6724   0.546    0.737    0.778    0.8613   0.7085   0.8213
 0.08826  0.818    0.4866   0.159    0.4143   0.1007   0.7773   0.487
 0.5225   0.8984   0.4907   0.525    0.4075   0.2632   0.2292   0.134
 0.4622   0.65     0.294    0.607    0.2725   0.2603   0.9326   0.787
 0.9478   0.941    0.3066   0.2944   0.3928   0.73     0.1797   0.2157
 0.609    0.4216   0.8984   0.8477   0.863    0.2478   0.993    0.6274
 0.724    0.03668  0.0991   0.5825   0.662    0.6904   0.7017   0.2379
 0.514    0.1646   0.3245   0.03072  0.3232   0.907    0.9966   0.6396
 0.2969   0.02539  0.66     0.764    0.7803   0.515    0.04074  0.2258
 0.08887  0.1782   0.875    0.1517   0.2351   0.3848   0.5933   0.6875
 0.1969   0.1283   0.06232  0.4348   0.168    0.6904   0.5464   0.12036
 0.885    0.007717 0.5967   0.2856   0.628    0.62     0.854    0.4297
 0.733    0.2274   0.9736   0.01622  0.456    0.4763   0.9707   0.874
 0.8794   0.511    0.1628   0.03458  0.506    0.1464   0.3674   0.1532
 0.786    0.3809   0.406    0.015434 0.901    0.951    0.3018   0.3584
 0.5337   0.4983   0.85     0.833    0.7324   0.492    0.39     0.09845
 0.8965   0.862    0.4033   0.181    0.2203   0.3738   0.2761   0.9653
 0.3577   0.289    0.3167   0.91     0.2688   0.3972   0.585    0.2178
 0.307    0.4966   0.513    0.5225   0.786    0.1888   0.9287   0.5093
 0.1193   0.3987   0.799    0.9995   0.611    0.9897   0.7515   0.4478
 0.3232   0.2426   0.3323   0.7134   0.77     0.7275   0.02043  0.3132
 0.3555   0.03122  0.8623   0.4705   0.6357   0.3157   0.5063   0.1711
 0.885    0.7554   0.815    0.0213   0.4346   0.049    0.905    0.525
 0.921    0.02411  0.771    0.7227   0.1786   0.278    0.03387  0.7744
 0.05875  0.8955   0.8374   0.715    0.3765   0.02075  0.675    0.9883
 0.63     0.7017   0.299    0.92     0.1644   0.3977   0.487    0.818
 0.636    0.3452   0.6406   0.783    0.3728   0.1619   0.7725   0.4673
 0.297    0.9375   0.083    0.0914   0.6704   0.08923  0.332    0.0973
 0.507    0.201    0.1658   0.2358   0.8706   0.6846   0.6396   0.289
 0.831    0.669    0.4683   0.2568   0.219    0.616    0.978    0.1564
 0.925    0.4265   0.6055   0.7246   0.235    0.5376   0.03668  0.2441
 0.7935   0.383    0.2996   0.3523   0.2544   0.6006   0.8896   0.757
 0.7134   0.3196   0.3657   0.249    0.2429   0.921    0.877    0.728
 0.8853   0.1635   0.546    0.9243   0.676    0.4749   0.3928   0.4187
 0.612    0.3953   0.2372   0.4092   0.1523   0.1599   0.03108  0.1602
 0.2474   0.3572   0.0643   0.9434   0.52     0.8574   0.959    0.7593
 0.2318   0.5444   0.2222   0.3884   0.8066   0.4573   0.664    0.335
 0.02025  0.1519   0.01386  0.989    0.852    0.695    0.01289  0.3433
 0.2148   0.9404   0.6753   0.704    0.11163  0.675    0.5264   0.1514
 0.5273   0.9785   0.2769   0.4846   0.2747   0.558    0.742    0.681
 0.835    0.9546   0.941    0.588    0.785    0.2095   0.07294  0.4343
 0.086    0.5825   0.513    0.6313   0.04236  0.4072   0.558    0.681
 0.4805   0.492    0.625    0.7744   0.002626 0.662    0.9043   0.4766
 0.6597   0.6934   0.3394   0.05453  0.9146   0.2222   0.7925   0.605
 0.812    0.671    0.4329   0.2118   0.363    0.1444   0.0955   0.692
 0.675    0.3      0.6846   0.535    0.9834   0.929    0.3582   0.964
 0.3835   0.1466   0.801    0.954    0.2554   0.01357  0.6636   0.8325
 0.6494   0.817    0.2268   0.00904  0.0487   0.08716  0.6753   0.3833
 0.663    0.396    0.6685   0.983    0.0728   0.694    0.02364  0.137
 0.1727   0.231    0.7896   0.8057   0.478    0.883    0.1785   0.5938
 0.11456  0.6997   0.1945   0.02365  0.7236   0.8623   0.2178   0.1295
 0.3867   0.7188   0.11475  0.6      0.419    0.2673   0.4404   0.0107
 0.4304   0.1364   0.3708   0.1158   0.1714   0.3123   0.3403   0.7163
 0.079    0.6245   0.719    0.558    0.4526   0.09924  0.512    0.2452
 0.519    0.999    0.7207   0.5605   0.7217   0.653    0.1164   0.789
 0.4724   0.2727   0.10315  0.9644   0.7573   0.06464  0.858    0.7847
 0.958    0.618    0.9536   0.46     0.9766   0.4263   0.4363   0.4434
 0.95     0.3032   0.4338   0.809    0.1642   0.0561   0.2668   0.1853
 0.356    0.934    0.968    0.327    0.913    0.434    0.6616   0.00502
 0.05066  0.5327   0.276    0.5176   0.0674   0.6143   0.8345   0.2976
 0.315    0.6646   0.527    0.791    0.0299   0.4558   0.8354   0.3115
 0.3735   0.3582   0.742    0.2637   0.8877   0.7603   0.4568   0.2045
 0.4746   0.392    0.65     0.391    0.972    0.6973   0.2297   0.568
 0.49     0.1895   0.547    0.79     0.747    0.5205   0.313    0.3809
 0.7817   0.32     0.1012   0.339    0.716    0.8955   0.8564   0.126
 0.6597   0.228    0.1194   0.4775   0.173    0.0265   0.7456   0.859
 0.4841   0.595    0.4553   0.1351   0.2246   0.3564   0.1832   0.8535
 0.703    0.2423   0.04187  0.145    0.997    0.1919   0.571    0.8555
 0.1578   0.2688   0.405    0.3909   0.1428   0.863    0.7295   0.3267
 0.1294   0.5986   0.677    0.7065   0.8853   0.923    0.9385   0.935
 0.1747   0.32     0.2292   0.2676   0.1161   0.4666   0.3826   0.2588
 0.1863   0.7993   0.3984   0.2961   0.2952   0.3247   0.923    0.05746 ]
Output (dst_gm):
[9.980e-01 5.364e-06 9.629e-01 2.682e-06 9.946e-01 6.676e-06 9.966e-01
 7.510e-06 9.995e-01 5.424e-06 9.888e-01 6.378e-06 9.990e-01 6.735e-06
 9.971e-01 5.484e-06]

Parent topic: Reduction Computation