Documentation

ReduceMin

Function Usage

Obtains the minimum value and its corresponding index position among the input data. For details about reduction instructions, see Reduction Instructions. For details about the principles of ReduceMin computation, see ReduceMax.

Prototype

  • Computation of the first n data elements of a tensor 
    1
2
    		 
    template <typename T>
__aicore__ inline void ReduceMin(const LocalTensor<T>& dstLocal, const LocalTensor<T>& srcLocal, const LocalTensor<T>& workLocal, const int32_t count, bool calIndex = 0)
  • High-dimensional tensor sharding computation
    • Bitwise mask mode 
      1
2
      		 
      template <typename T>
__aicore__ inline void ReduceMin(const LocalTensor<T>& dstLocal, const LocalTensor<T>& srcLocal, const LocalTensor<T>& workLocal, const uint64_t mask[], const int32_t repeatTimes, const int32_t srcRepStride, bool calIndex = 0)
    • Contiguous mask mode 
      1
2
      		 
      template <typename T>
__aicore__ inline void ReduceMin(const LocalTensor<T>& dstLocal, const LocalTensor<T>& srcLocal, const LocalTensor<T>& workLocal, const int32_t mask, const int32_t repeatTimes, const int32_t srcRepStride, bool calIndex = 0)

Parameters

Table 1 Template parameters

Parameter

Description

T

Operand data type.

For the Atlas Training Series Product , the supported data type is half.
Table 2 Parameters

Parameter

Input/Output

Meaning

dstLocal

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).

srcLocal

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.

workLocal

Input

Space required by some hardware models to store intermediate results during API execution. The space must meet the minimum space requirement. For details about the computation method, see the ReduceMax computation diagram.

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.

Atlas Training Series Product : workLocal is required.

count

Input

Number of elements of the input data.

The parameter value range is related to the operand data type. The maximum number of elements that can be processed varies according to the data type. However, the maximum size of data that can be processed cannot exceed the UB size limit.

calIndex

Input

A bool that specifies whether to obtain the minimum value with index. Defaults to false.

  • true: obtains the minimum value with index.
  • false: obtains the minimum value without index.

mask

Input

mask is used to control the elements that participate in computation in each iteration.

  • 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 iteration 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].
  • 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 parameter type is a uint64_t array whose length is 2.

    For example, if mask = [0, 8] and 8 = 0b1000, only the fourth element participates in computation.

    The parameter value range is related to the operand data type. The maximum number of elements that can be processed in each iteration varies according to the data type. When the operand is 16-bit, mask[0] and mask[1] ∈ [0, 264 -1] and cannot be 0 at the same time. When the operand is 32-bit, mask[1] is 0 and mask[0] ∈ (0, 264 – 1]. When the operand is 64-bit, mask[1] is 0 and mask[0] ∈ (0, 232 – 1].

repeatTimes

Input

Number of repeats (iterations). In contrast with Common Parameters, this parameter supports a larger value range. Ensure that the value does not exceed the maximum value of int32_t.

srcRepStride

Input

Address stride between adjacent iterations of the source operand, that is, the number of data blocks skipped in each iteration of the source operand. For details, see repeatStride.

Returns

None

Availability

Atlas Training Series Product

Precautions

  • To save address space, developers can define a tensor for srcLocal, dstLocal, and workLocal to use at the same time (address overlapping). The constraints on address overlapping are as follows:
    • If there is a dependency between the source operand and the destination operand, that is, the destination operand of the Nth iteration is the source operand of the (N + 1)th iteration, address overlapping is not allowed.
    • When workLocal needs to be used and the addresses of dstLocal and workLocal overlap (dstLocal space is generally smaller than workLocal space), workLocal must meet the minimum space requirement. Otherwise, address overlapping is not supported.
    • If the addresses of the operands overlap, the addresses must be completely overlapped. Partial overlapping is not supported.
  • The dstLocal result is stored in the sequence of minimum value and index. If no index is required, only the minimum value is 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. However, the index values would be incorrect if they are read based on the half type and must be converted to the integer type using the reinterpret_cast method. If the input type is half, reinterpret_cast<uint16_t*> is required. If the input type is float, reinterpret_cast<uint32_t*> is required. For example, in the complete example of calling the high-dimensional tensor sharding computation API, the computation result is [0.01034, 2.104e-05]. The reinterpret_cast method is required for converting 2.104e-05 to yield the index value 353. The following is a conversion example: 
    1
2
    		 
    float minIndex = dst.GetValue(1);
uint32_t realIndex = *reinterpret_cast<uint32_t *>(&minIndex);
  • If multiple minimum values exist, the index of the first minimum value is returned.
  • When the input type is half, the obtained index value cannot be greater than 65535 (the maximum that can be represented by uint16_t).

Example

  • Example of high-dimensional tensor sharding computation (contiguous mask mode) 
    1
2
3
    		 
    // dstLocal, srcLocal, and workLocal are of the half type. For srcLocal, the computation data is of size 8320 and is continuously arranged. It requires indexes and uses the high-dimensional tensor sharding computation API. repeatTimes is set to 65. mask is set to involving all elements in the computation.
uint64_t mask = 128;
AscendC::ReduceMin<half>(dstLocal, srcLocal, workLocal, mask, 65, 8, true);
  • Example of high-dimensional tensor sharding computation (bitwise mask mode) 
    1
2
3
    		 
    // dstLocal, srcLocal, and workLocal are of the half type. For srcLocal, the computation data is of size 8320 and is continuously arranged. It requires indexes and uses the high-dimensional tensor sharding computation API. repeatTimes is set to 65. mask is set to involving all elements in the computation.
uint64_t mask[2] = { 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF };
AscendC::ReduceMin<half>(dstLocal, srcLocal, workLocal, mask, 65, 8, true);
  • Example of computing the first n data elements of a tensor 
    1
2
    		 
    // dstLocal, srcLocal, and workLocal are of the half type. For srcLocal, the computation data is of size 8320 and is continuously arranged. It requires indexes and uses the computation API for the first n tensor elements.
AscendC::ReduceMin<half>(dstLocal, srcLocal, workLocal, 8320, true);
  • The following is a complete example of calling the high-dimensional tensor sharding computation API: 
     1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
    		 
    #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(workQueue, 1, 32 * sizeof(half)); // Based on the formula, the required minimum work space is 32, that is, 64 bytes.
        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::LocalTensor<half> workLocal = workQueue.AllocTensor<half>();
 
        AscendC::ReduceMin<half>(dstLocal, srcLocal, workLocal, mask, repeat, repStride, true);
        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc.FreeTensor(srcLocal);
        workQueue.FreeTensor(workLocal);
    }
    __aicore__ inline void CopyOut()
    {
        AscendC::LocalTensor<half> dstLocal = outQueueDst.DeQue<half>();
        AscendC::DataCopy(dstGlobal, dstLocal, srcDataSize);
        outQueueDst.FreeTensor(dstLocal);
    }
private:
    AscendC::TPipe pipe;
    AscendC::TQue<AscendC::QuePosition::VECIN, 1> inQueueSrc;
    AscendC::TQue<AscendC::QuePosition::VECOUT, 1> workQueue;
    AscendC::TQue<AscendC::QuePosition::VECOUT, 1> outQueueDst;
    AscendC::GlobalTensor<half> srcGlobal, dstGlobal;
    int srcDataSize = 512;
    int dstDataSize = 512;
    int mask = 128;
    int repStride = 8;
    int repeat = 0;
};
extern "C" __global__ __aicore__ void kernel_ReduceMin_lv0_half_512(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
{
    KernelReduce op;
    op.Init(src, dstGm);
    op.Process();
}

    The following is an example:

    Input (src_gm):
[0.769    0.8584   0.1082   0.2715   0.1759   0.7646   0.6406   0.2944   0.4255   0.927    0.8022   0.04507  0.9688   0.919    0.3008   0.7144   0.3206   0.6753   0.8276
 0.3374   0.4636   0.3591   0.112    0.93     0.822    0.7314   0.01165  0.31     0.5586   0.2808   0.3997   0.04544  0.0931   0.8438   0.612    0.03052  0.3652   0.1153
 0.06213  0.12103  0.4421   0.8003   0.1583   0.845    0.125    0.6934   0.4592   0.871    0.573    0.4133   0.885    0.6875   0.2854   0.7007   0.1294   0.2092   0.3794
 0.7534   0.5923   0.03888  0.2412   0.8584   0.6704   0.429    0.77     0.427    0.6323   0.524    0.0519   0.514    0.2408   0.09357  0.1702   0.3694   0.665    0.2651
 0.9507   0.661    0.459    0.1317   0.7334   0.289    0.0325   0.1187   0.6626   0.2769   0.3083   0.923    0.826    0.7275   0.976    0.4854   0.724    0.7783   0.8022
 0.677    0.2401   0.377    0.839    0.2297   0.54     0.743    0.511    0.1346   0.7183   0.4775   0.3442   0.561    0.2935   0.04065  0.1001   0.753    0.6816   0.8955
 0.07324  0.5947   0.508    0.2229   0.468    0.3135   0.0898   0.5625   0.7407   0.803    0.1071   0.6724   0.797    0.8296   0.807    0.8604   0.7437   0.967    0.4307
 0.3833   0.03394  0.02478  0.9385   0.3105   0.43     0.0706   0.4363   0.05832  0.0812   0.2418   0.03967  0.557    0.2705   0.963    0.8125   0.342    0.8853   0.3047
 0.7197   0.7173   0.02887  0.7695   0.4304   0.691    0.4285   0.9917   0.3994   0.19     0.3984   0.1888   0.83     0.0644   0.9766   0.857    0.09784  0.831    0.224
 0.8228   0.8975   0.1775   0.725    0.882    0.7188   0.3257   0.05347  0.1026   0.05902  0.9697   0.445    0.728    0.626    0.3577   0.711    0.2343   0.3865   0.03888
 0.3318   0.855    0.891    0.3647   0.9297   0.5083   0.7163   0.5737   0.2155   0.804    0.2118   0.525    0.1116   0.558    0.05203  0.6343   0.5796   0.5605   0.449
 0.4475   0.3713   0.3708   0.11017  0.2048   0.087    0.265    0.937    0.933    0.4683   0.5884   0.4312   0.9326   0.839    0.592    0.566    0.4229   0.05493  0.4578
 0.353    0.2915   0.8345   0.888    0.8394   0.8774   0.3582   0.2913   0.798    0.87     0.3372   0.6914   0.9185   0.4368   0.3276   0.8125   0.782    0.885    0.6543
 0.1626   0.0965   0.8247   0.03952  0.459    0.5596   0.694    0.59     0.02153  0.3762   0.2428   0.9727   0.3672   0.732    0.2676   0.2102   0.128    0.5957   0.988
 0.583    0.9097   0.144    0.3845   0.2151   0.327    0.2925   0.974    0.771    0.9224   0.147    0.6206   0.1774   0.1415   0.7637   0.573    0.9736   0.183    0.837
 0.0753   0.098    0.8184   0.08527  0.889    0.528    0.2207   0.1852   0.5903   0.594    0.04865  0.5806   0.6006   0.2048   0.4934   0.1302   0.7217   0.949    0.04105
 0.6875   0.3975   0.845    0.6045   0.4077   0.01927  0.1505   0.4407   0.8457   0.9614   0.4504   0.7134   0.07837  0.3557   0.521    0.545    0.02188  0.581    0.3215
 0.4458   0.853    0.4656   0.928    0.2927   0.3467   0.3516   0.1686   0.88     0.1509   0.2993   0.4006   0.611    0.1251   0.0887   0.896    0.2651   0.5596   0.0359
 0.6895   0.3494   0.871    0.673    0.1486   0.7812   0.0925   0.434    0.09985  0.02402  0.2932   0.01034  0.744    0.6357   0.658    0.1487   0.3416   0.1171   0.3088
 0.557    0.837    0.10944  0.7036   0.9097   0.3706   0.73     0.2844   0.78     0.5117   0.5537   0.776    0.6553   0.128    0.3184   0.8022   0.686    0.1785   0.2212
 0.74     0.8955   0.4773   0.6084   0.7827   0.239    0.4849   0.1816   0.2854   0.166    0.012505 0.4421   0.2179   0.06094  0.2124   0.409    0.641    0.1841   0.776
 0.4685   0.2334   0.4094   0.3447   0.6836   0.434    0.10516  0.514    0.8345   0.371    0.8555   0.5396   0.844    0.7554   0.171    0.749    0.7344   0.05936  0.4482
 0.9873   0.3137   0.7627   0.871    0.5503   0.956    0.2607   0.0904   0.535    0.3079   0.762    0.793    0.545    0.889    0.8936   0.6094   0.6533   0.5737   0.945
 0.4434   0.2686   0.05872  0.0776   0.0915   0.5386   0.6777   0.3164   0.8955   0.3398   0.3801   0.3784   0.3904   0.4849   0.816    0.962    0.335    0.705    0.1871
 0.3643   0.7163   0.6484   0.4526   0.8096   0.2408   0.608    0.0215   0.7246   0.412    0.609    0.03342  0.653    0.0424   0.672    0.627    0.3025   0.9424   0.3784
 0.1012   0.4192   0.7695   0.7383   0.9395   0.06494  0.3027   0.11523  0.6035   0.1727   0.4048   0.932    0.4053   0.3528   0.8193   0.0355   0.01953  0.574    0.509
 0.1443   0.0848   0.568    0.8716   0.968    0.613    0.535    0.0389   0.84     0.0655   0.127    0.06104  0.526    0.504    0.4175   0.8027   0.482    0.304   ]
Output (dst_gm):
In [0.01034, 2.104e-05], 2.104e-05 is converted using the reinterpret_cast method to obtain the index value 353.
  • The following is a complete example of calling the computation API for the first n data elements of a tensor: 
     1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
    		 
    #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(workQueue, 1, 32 * sizeof(half)); // Based on the formula, the required minimum work space is 32, that is, 64 bytes.
        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::LocalTensor<half> workLocal = workQueue.AllocTensor<half>();
       
        // level2
        AscendC::ReduceMin<half>(dstLocal, srcLocal, workLocal, srcDataSize, true);
        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc.FreeTensor(srcLocal);
        workQueue.FreeTensor(workLocal);
    }
    __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::QuePosition::VECIN, 1> inQueueSrc;
    AscendC::TQue<AscendC::QuePosition::VECOUT, 1> workQueue;
    AscendC::TQue<AscendC::QuePosition::VECOUT, 1> outQueueDst;
    AscendC::GlobalTensor<half> srcGlobal, dstGlobal;
    int srcDataSize = 288;
    int dstDataSize = 16;
    int mask = 128;
    int repStride = 8;
    int repeat = 0;
};
extern "C" __global__ __aicore__ void kernel_ReduceMin_lv2_half_288(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
{
    KernelReduce op;
    op.Init(src, dstGm);
    op.Process();
}

    The following is an example:

    Result example:
Input (src_gm):
[0.556    0.5225   0.3623   0.214    0.556    0.0643   0.769    0.594    0.261    0.3652   0.911    0.924    0.386    0.3696   0.2296   0.5957   0.1709   0.79     0.8516
 0.341    0.705    0.728    0.8135   0.7534   0.5874   0.771    0.05835  0.7456   0.1049   0.3105   0.1729   0.9253   0.8003   0.918    0.5005   0.7744   0.688    0.6807
 0.1456   0.4136   0.1055   0.12054  0.275    0.3848   0.08405  0.3843   0.3218   0.6904   0.878    0.3706   0.3586   0.3518   0.429    0.7275   0.6123   0.8096   0.563
 0.54     0.8857   0.8594   0.4143   0.525    0.2744   0.1376   0.382    0.6406   0.1534   0.134    0.2993   0.365    0.8843   0.2986   0.00393  0.6577   0.313    0.8164
 0.8706   0.7686   0.873    0.3286   0.03787  0.8145   0.4656   0.66     0.1362   0.1075   0.1376   0.9097   0.9214   0.833    0.3657   0.8438   0.006973 0.2408   0.801
 0.1862   0.864    0.8745   0.1805   0.4324   0.8647   0.844    0.8936   0.8496   0.311    0.0334   0.3967   0.579    0.43     0.2332   0.5366   0.3557   0.3542   0.945
 0.9336   0.252    0.4375   0.9727   0.859    0.6294   0.6787   0.8887   0.1884   0.524    0.787    0.04755  0.3984   0.0508   0.4065   0.716    0.3184   0.21     0.10645
 0.7544   0.2827   0.7856   0.4878   0.5903   0.12146  0.6426   0.8438   0.063    0.7617   0.6396   0.1995   0.6475   0.1464   0.7617   0.514    0.3506   0.2708   0.8643
 0.1204   0.04337  0.21     0.528    0.0644   0.2133   0.0643   0.0125   0.602    0.654    0.866    0.225    0.9473   0.408    0.4597   0.2793   0.11145  0.293    0.04156
 0.7705   0.3555   0.3977   0.7485   0.76     0.9824   0.2832   0.1239   0.4915   0.878    0.5986   0.7217   0.832    0.6206   0.6455   0.0639   0.772    0.01854  0.7437
 0.1962   0.485    0.5483   0.414    0.9253   0.2452   0.2942   0.9478   0.879    0.586    0.659    0.635    0.7197   0.933    0.08905  0.02892  0.74     0.499    0.02054
 0.2241   0.5137   0.8325   0.185    0.6196   0.949    0.935    0.5605   0.04108  0.3672   0.5566   0.3958   0.4565   0.8135   0.3015   0.46     0.1196   0.5044   0.54
 0.05203  0.687    0.8525   0.501    0.3464   0.307    0.804    0.0926   0.202    0.999    0.955    0.581    0.06216  0.271    0.9365   0.854    0.4202   0.269    0.985
 0.04547  1.       0.1208   0.5225   0.00935  0.4128   0.644    0.3826   0.6963   0.2942   0.007626 0.7144   0.609    0.3206   0.694    0.393    0.6265   0.6904   0.2487
 0.9478   0.798    0.891    0.8867   0.9414   0.395    0.11285  0.515    0.919    0.013855 0.749    0.5527   0.465    0.451    0.1458   0.59     0.893    0.0146   0.062
 0.06604  0.934    0.2242  ]
Output (dst_gm):
In [0.00393, 4.3e-06], 4.3e-06 is converted using the reinterpret_cast method to obtain the index value 72.
Parent topic: Reduction Instructions