BlockReduceMin

Function Usage

Computes the maximum of all elements in each data block. For details about reduction instructions, see Reduction Instructions.

Prototype

Bitwise mask mode

        
             template <typename T, bool isSetMask = true>
__aicore__ inline void BlockReduceMin (const LocalTensor<T>& dstLocal, const LocalTensor<T>& srcLocal,const int32_t repeat, const uint64_t mask[], const int32_t dstRepStride, const int32_t srcBlkStride, const int32_t srcRepStride)

Contiguous mask mode

        
             template <typename T, bool isSetMask = true>
__aicore__ inline void BlockReduceMin (const LocalTensor<T>& dstLocal, const LocalTensor<T>& srcLocal,const int32_t repeat, const int32_t maskCount, const int32_t dstRepStride, const int32_t srcBlkStride, const int32_t srcRepStride)

Parameters

**Table 1** Parameters in the template
Parameter	Description
T	Operand data type. For the Atlas Training Series Product , 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 MASK_PLACEHOLDER.

**Table 2** Parameters
Parameter	Input/Output	Description
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 16-byte aligned (for data of the half type) or 32-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.
repeat	Input	Number of iteration repeats. The value range is [0, 255]. For details about this parameter, see Common Parameters.
mask[2]/ maskCount	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].
dstRepStride	Input	Address stride between adjacent iterations of the destination operand. The unit is the length after reduction of a repeat. After each repeat (eight data blocks) is reduced, eight elements are obtained. Therefore, when the input type is half, the unit of RepStride is 16 bytes. When the input type is float, the unit of RepStride is 32 bytes. Note that this parameter cannot be set to 0 for the Atlas Training Series Product .
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. For details, see repeatStride.

Returns

None

Availability

Atlas Training Series Product

Precautions

To save memory space, you can define a tensor shared by the source and destination operands (by address overlapping). Note that the computed destination operand data cannot overwrite the source operands that are not involved in the computation. Exercise caution when defining the tensor.
For details about the alignment requirements of the operand address offset, see General Restrictions.
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.

Example

This example shows only part of the code used in the computation process. To run the sample code, copy the code snippet and replace part of code of the Compute function in Template Sample.

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

        
             uint64_t mask = 256/sizeof(half);
int repeat = 1;
// repeatTimes = 1, 128 elements one repeat, 128 elements total
// srcBlkStride = 1, no gap between blocks in one repeat
// dstRepStride = 1, srcRepStride = 8, no gap between repeats
AscendC::BlockReduceMin<half>(dstLocal, srcLocal, repeat, mask, 1, 1, 8);

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

        
             uint64_t mask[2] = { UINT64_MAX, UINT64_MAX };
int repeat = 1;
// repeatTimes = 1, 128 elements one repeat, 128 elements total
// srcBlkStride = 1, no gap between blocks in one repeat
// dstRepStride = 1, srcRepStride = 8, no gap between repeats
AscendC::BlockReduceMin<half>(dstLocal, srcLocal, repeat, mask, 1, 1, 8);

Result example:

Input (src_gm):
[3.902, -8.719, -5.797, -7.969, -9.516, -6.457, 0.1114, 2.781,
 4.758, 0.01262, 2.367, 2.16, -4.473, -9.336, -7.375, 6.078, 
 ... 
 3.621, -5.852, 6.902, 0.332, 1.112, 2.697, -9, 9.938, 
 4.645, 0.7021, 7.598, 4.586, 0.6431, 4.781, 3.566, 4.004]  
Output (dst_gm):
[-9.516, ..., -9, 0, ..., 0]

Parent topic: Reduction Instructions