WholeReduceMax

Applicability

Product	Supported/Unsupported
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	√

Functions

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** Parameters in the template
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 the 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 source operand must have the same data type as the destination operand.
mask/mask[]	Input	The mask parameter is used to control the elements involved in computation in each iteration. Bitwise mode: controls the elements that participate in computation by bit. If a bit is set to 1, the corresponding element participates in the computation. If a bit is set to 0, the corresponding element is masked in the computation. The mask is in array form. The array length and the value range of the array elements are related to the data type of the operand. When the operand is 16-bit, the array length is 2. In this case, mask[0] and mask[1] must be in the range of [0, 2⁶⁴ – 1] and cannot be 0 at the same time. When the operand is 32-bit, the array length is 1. In this case, mask[0] must be in the range of (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1. In this case, mask[0] must be in the range of (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 minimum value are returned, the unit is twice the length of the data type of dstLocal. For example, if dst is half, the unit is 4 bytes. When only the maximum/minimum value is returned, the unit is the number of bytes occupied by the dst data type. 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 Environment Variables.
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	Specifies the relative position between the index and value in dstLocal and the return behavior. The parameter is of the ReduceOrder type. The default value is 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

Constraints

For details about the operand address alignment requirements, see General Address Alignment Restrictions.
For details about the operand address overlapping restrictions, see General Address Overlap Restrictions.

The order of storing the dst result is determined by the order parameter. By default, the maximum value and maximum value index are stored. In the returned result, the indexes are stored based on the data type of dstLocal. For example, if dstLocal uses the half type, the indexes are stored based on the half type. The reinterpret_cast method is used to convert the indexes to the corresponding integer type when they are 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], use the reinterpret_cast method to convert 5.364e-06 and obtain the index value 90. For Atlas A2 training products / Atlas A2 inference products , Atlas A3 training products / Atlas A3 inference products , and ORDER_ONLY_INDEX (only the maximum or minimum index is returned), reinterpret_cast<uint32_t*> must be used to read the index.
Proper use of the reduction instruction in different scenarios can improve performance. For details about the introduction, see Using the Reduction Instruction Properly in Different Scenarios. For details about examples, see ReduceCustom.

Examples

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, the values of repeatTime, dstRepStride, srcBlkStride, and srcRepStride are 4, 1, 1, and 8, respectively.
// 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, the values of repeatTime, dstRepStride, srcBlkStride, and srcRepStride are 4, 1, 1, and 8, respectively.
// 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