Reciprocal
Applicability
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Atlas 350 Accelerator Card |
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Function Usage
Computes the reciprocal element-wise. The formula is as follows:

Prototype
- Computation of the first n data elements of a tensor
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template <typename T, const ReciprocalConfig& config = DEFAULT_RECIPROCAL_CONFIG> __aicore__ inline void Reciprocal(const LocalTensor<T>& dst, const LocalTensor<T>& src, const int32_t& count)
- High-dimensional tensor sharding computation
- Bitwise mask mode
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template <typename T, bool isSetMask = true, const ReciprocalConfig& config = DEFAULT_RECIPROCAL_CONFIG> __aicore__ inline void Reciprocal(const LocalTensor<T>& dst, const LocalTensor<T>& src, uint64_t mask[], const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)
- Contiguous mask mode
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template <typename T, bool isSetMask = true, const ReciprocalConfig& config = DEFAULT_RECIPROCAL_CONFIG> __aicore__ inline void Reciprocal(const LocalTensor<T>& dst, const LocalTensor<T>& src, uint64_t mask, const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)
- Bitwise mask mode
Parameters
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Parameter |
Description |
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T |
Operand data type. For the Atlas 350 Accelerator Card, the supported data types are half, float, int64_t, and uint64_t. For the For the For the For the For the |
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isSetMask |
Indicates whether to set mask inside the API.
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config |
This parameter is supported only by the Atlas 350 Accelerator Card. Subnormal computation mode. The ReciprocalConfig type is defined as follows:
The algo parameter in the ReciprocalConfig structure is used to configure the subnormal computation mode. The options of algo are as follows:
The default value of this parameter, DEFAULT_RECIPROCAL_CONFIG, is defined as follows:
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Parameter |
Input/Output |
Description |
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dst |
Output |
Destination operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned. |
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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. |
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count |
Input |
Number of elements involved in the computation. |
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mask[]/mask |
Input |
mask controls the elements that participate in computation in each iteration.
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repeatTime |
Input |
Number of iteration repeats. The vector compute unit reads 256 bytes of contiguous data for computation each time. To process the input data, the data needs to be read and computed over multiple repeats. repeatTime indicates the number of repeats. For details about this parameter, see High-dimensional Sharding APIs. |
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repeatParams |
Input |
Parameters that control the operand address strides. They are of the UnaryRepeatParams type (see UnaryRepeatParams), and contain parameters such as the address stride of the operand for the same Data Block between adjacent iterations and the address stride of the operand between different Data Blocks in a single iteration. For details about the address stride of the operand between adjacent iterations, see repeatStride. For details about the address stride of the operand between different data blocks in a single iteration, see dataBlockStride. |
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.
- If the value of src is 0, an unknown result may be produced.
- Using the Reciprocal API, the operator computation result fails to meet the dual-0.1% error limit (the error ratio is within 0.1% and the relative error is within 0.1%) with input of the half type, and fails to meet the dual-0.01% error limit with input of the float type. If the accuracy requirement is high, the Div API is preferred.
- For the Atlas 350 Accelerator Card, int64_t and uint64_t support only the APIs that operate on the first n data elements of a tensor.
Examples
In the examples, both srcLocal and dstLocal are of half type.
For more examples, see LINK.
- Example of high-dimensional tensor sharding computation (contiguous mask mode)
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uint64_t mask = 256 / sizeof(half); // repeatTime = 4, 128 elements one repeat, 512 elements total // dstBlkStride, srcBlkStride = 1, no gap between blocks in one repeat // dstRepStride, srcRepStride = 8, no gap between repeats AscendC::Reciprocal(dstLocal, srcLocal, mask, 4, { 1, 1, 8, 8 });
- Example of high-dimensional tensor sharding computation (bitwise mask mode)
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uint64_t mask[2] = { UINT64_MAX, UINT64_MAX }; // repeatTime = 4, 128 elements one repeat, 512 elements total // dstBlkStride, srcBlkStride = 1, no gap between blocks in one repeat // dstRepStride, srcRepStride = 8, no gap between repeats AscendC::Reciprocal(dstLocal, srcLocal, mask, 4, { 1, 1, 8, 8 });
- Example of computing the first n data elements of a tensor
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AscendC::Reciprocal(dstLocal, srcLocal, 512); static constexpr ReciprocalConfig config = { ReciprocalAlgo::PRECISION_1ULP_FTZ_FALSE }; AscendC::Reciprocal<T, config>(dstLocal, srcLocal, 512);
Input (srcLocal): [-7.152 -7.24 1.771 ... -1.339 4.473] Output (dstLocal): [-0.1396 -0.1382 0.5645 ... -0.748 0.2231]