ReduceMin

Applicability

Product	Supported
Atlas 350 Accelerator Card	√
Atlas A3 training product / Atlas A3 inference product	√
Atlas A2 training product / Atlas A2 inference product	√
Atlas 200I/500 A2 inference product	√
Atlas inference product AI Core	√
Atlas inference product Vector Core	x
Atlas training product	√

Function Usage

Obtains the minimum value and its corresponding index position among the input data. For details about reduction instructions, see How to Use Reduction Compute APIs. For details about the ReduceMin computation principle, see ReduceMax.

Prototype

Computation of the first n data elements of a tensor

        
             template <typename T>
__aicore__ inline void ReduceMin(const LocalTensor<T>& dst, const LocalTensor<T>& src, const LocalTensor<T>& sharedTmpBuffer, const int32_t count, bool calIndex = 0)

High-dimensional tensor sharding computation

Bitwise mask mode

          
               template <typename T>
__aicore__ inline void ReduceMin(const LocalTensor<T>& dst, const LocalTensor<T>& src, const LocalTensor<T>& sharedTmpBuffer, const uint64_t mask[], const int32_t repeatTime, const int32_t srcRepStride, bool calIndex = 0)

Contiguous mask mode

          
               template <typename T>
__aicore__ inline void ReduceMin(const LocalTensor<T>& dst, const LocalTensor<T>& src, const LocalTensor<T>& sharedTmpBuffer, const int32_t mask, const int32_t repeatTime, const int32_t srcRepStride, bool calIndex = 0)

Parameters

**Table 1** Parameters in the template
Parameter	Description
T	Operand data type. For the Atlas 350 Accelerator Card, the supported data types are uint16_t, int16_t, uint32_t, int32_t, half, float, uint64_t, and int64_t. For the Atlas A3 training product / Atlas A3 inference product , the supported data types are half and float. For the Atlas A2 training product / Atlas A2 inference product , the supported data types are half and float. For the Atlas 200I/500 A2 inference product , the supported data types are half and float. For the Atlas inference product AI Core, the supported data types are half and float. For the Atlas training product , the supported data type is half.

**Table 2** Parameters
Parameter	Input/Output	Meaning
dst	Output	Destination operand. The type is LocalTensor, and TPosition can be 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 TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned. The source operand must have the same data type as the destination operand.
sharedTmpBuffer	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 TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned. Its data type must match that of the destination operand. For the Atlas 350 Accelerator Card, due to different internal algorithm implementations of the API, sharedTmpBuffer is not required. You can directly pass src or a sharedTmpBuffer of any size. For the Atlas A3 training product / Atlas A3 inference product , sharedTmpBuffer is required. For the Atlas A2 training product / Atlas A2 inference product , sharedTmpBuffer is required. For the Atlas 200I/500 A2 inference product , sharedTmpBuffer is required. For the Atlas inference product AI Core, sharedTmpBuffer is required. For the Atlas training product , sharedTmpBuffer is required.
count	Input	Number of elements involved in the computation. 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.
mask/mask[]	Input	mask controls the elements that participate 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 from the computation. 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, with mask[0] and mask[1] each in the range [0, 2⁶⁴ – 1], and they cannot both be 0 at the same time. When the operand is 32-bit, the array length is 1, with mask[0] in the range (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1, with mask[0] in the range (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 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].
repeatTime	Input	Number of iteration repeats. Unlike High-dimensional Sharding APIs, a larger value range is supported. 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 from the source operand in each iteration. For details, see repeatStride.
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.

Returns

None

Restrictions

For details about the operand address alignment requirements, see General Address Alignment Restrictions.
For details about the restrictions on operand address overlapping, see General Address Overlapping Restrictions. If sharedTmpBuffer is required, address overlapping between dst and sharedTmpBuffer is supported (usually dst requires less space than sharedTmpBuffer). However, sharedTmpBuffer must meet the minimum space requirement; otherwise, address overlapping is not supported.

Data is stored in dst in the order of minimum value and its index. If the index is not required, only the minimum value is stored. In the returned result, the indexes are stored based on the data type of dst. For example, if dst 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 to convert 2.104e-05 to the index value 353. The following is a conversion example:

        
             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).
For the Atlas 350 Accelerator Card, uint64_t and int64_t support only the API for computing the first n data elements of the tensor.

Examples

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

        
             // dstLocal, srcLocal, and sharedTmpBuffer 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. repeatTime is set to 65. mask is set to involving all elements in the computation.
int32_t mask = 128;
AscendC::ReduceMin<half>(dstLocal, srcLocal, sharedTmpBuffer, mask, 65, 8, true);

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

        
             // dstLocal, srcLocal, and sharedTmpBuffer 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. repeatTime 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, sharedTmpBuffer, mask, 65, 8, true);

Example of computing the first n data elements of a tensor

        
             // dstLocal, srcLocal, and sharedTmpBuffer are of the half type. For srcLocal, the computation data is of size 8320 and is continuously arranged. It requires indexes and uses the API for computing the first n data elements of a tensor.
AscendC::ReduceMin<half>(dstLocal, srcLocal, sharedTmpBuffer, 8320, true);

The following is a complete example of calling the high-dimensional tensor sharding computation API:

        
             #include "kernel_operator.h"

int srcDataSize = 512;
int dstDataSize = 512;
int mask = 128;
int repStride = 8;
int repeat = srcDataSize / mask;

// Initialize srcLocal, dstLocal, and sharedTmpBuffer.
AscendC::LocalTensor<half> srcLocal = inQueueSrc.DeQue<half>();
AscendC::LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();
AscendC::LocalTensor<half> sharedTmpBuffer = workQueue.AllocTensor<half>();

// With a mask of 128, it processes 128 elements at a time, repeating 4 times to complete the computation for all 512 elements. When calIndex is true, it retrieves the index of the minimum value.
AscendC::ReduceMin<half>(dstLocal, srcLocal, sharedTmpBuffer, mask, repeat, repStride, true);
// Release the tensor.
outQueueDst.EnQue<half>(dstLocal);
inQueueSrc.FreeTensor(srcLocal);
workQueue.FreeTensor(sharedTmpBuffer);

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 API for computing the first n data elements of a tensor:

        
             #include "kernel_operator.h"

int srcDataSize = 288;
// Initialize srcLocal, dstLocal, and sharedTmpBuffer.
AscendC::LocalTensor<half> srcLocal = inQueueSrc.DeQue<half>();
AscendC::LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();
AscendC::LocalTensor<half> sharedTmpBuffer = workQueue.AllocTensor<half>();

// The Level‑2 API computes the first 288 numbers. If calIndex is true, the index of the minimum value is returned.
AscendC::ReduceMin<half>(dstLocal, srcLocal, sharedTmpBuffer, srcDataSize, true);
// Release the tensor.
outQueueDst.EnQue<half>(dstLocal);
inQueueSrc.FreeTensor(srcLocal);
workQueue.FreeTensor(sharedTmpBuffer);

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 Computation