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10d80104创建于 3 天前历史提交

Cast

产品支持情况

产品

是否支持

Ascend 950PR/Ascend 950DT

√

Atlas A3 训练系列产品/Atlas A3 推理系列产品

√

Atlas A2 训练系列产品/Atlas A2 推理系列产品

√

Atlas 200I/500 A2 推理产品

√

Atlas 推理系列产品AI Core

√

Atlas 推理系列产品Vector Core

x

Atlas 训练系列产品

√

Kirin X90

√

Kirin 9030

√

功能说明

根据源操作数和目的操作数tensor的数据类型进行精度转换。

函数原型

  • tensor前n个数据计算。

    template <typename T, typename U>
__aicore__ inline void Cast(const LocalTensor<T>& dst, const LocalTensor<U>& src, const RoundMode& roundMode, const uint32_t count)

  • tensor高维切分计算。

    • mask逐bit模式。

      template <typename T, typename U, bool isSetMask = true>
__aicore__ inline void Cast(const LocalTensor<T>& dst, const LocalTensor<U>& src, const RoundMode& roundMode, const uint64_t mask[], const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

    • mask连续模式。

      template <typename T, typename U, bool isSetMask = true>
__aicore__ inline void Cast(const LocalTensor<T>& dst, const LocalTensor<U>& src, const RoundMode& roundMode, const uint64_t mask, const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

参数说明

表1 模板参数说明

参数名 描述
T 输出Tensor的数据类型。
U 输入Tensor的数据类型。
isSetMask 是否在接口内部设置mask。
• true，表示在接口内部设置mask。
• false，表示在接口外部设置mask，开发者需要使用SetVectorMask接口设置mask值。这种模式下，接口入参中的mask值设置为占位符MASK_PLACEHOLDER，用于占位，无实际含义。

表2 参数说明

参数名 输入/输出 描述
dst 输出 目的操作数。
类型为LocalTensor，支持的TPosition为VECIN/VECCALC/VECOUT。
src 输入 源操作数。
类型为LocalTensor，支持的TPosition为VECIN/VECCALC/VECOUT。
roundMode 输入 精度转换处理模式，类型是RoundMode。
RoundMode为枚举类型，用以控制精度转换处理模式，可参考精度转换规则。RoundMode取值说明请参考RoundMode取值说明。
count 输入 参与计算的元素个数。
注：参数取值范围和操作数的数据类型有关，数据类型不同，能够处理的元素个数最大值不同，最大处理的数据量不能超过UB大小限制。
mask/mask[] 输入 mask用于控制每次迭代内参与计算的元素。
注意：数据类型转换的mask会按照输入和输出类型中sizeof(dtype)较大的来筛选。
设置详见掩码操作。
repeatTime 输入 重复迭代次数。
矢量计算单元，每次读取连续的256Bytes数据进行计算，为完成对输入数据的处理，必须通过多次迭代（repeat）才能完成所有数据的读取与计算。repeatTime表示迭代的次数。
关于该参数的具体描述请参考高维切分。
repeatParams 输入 控制操作数地址步长的参数。UnaryRepeatParams类型，包含操作数相邻迭代间相同DataBlock的地址步长，操作数同一迭代内不同DataBlock的地址步长等参数。
相邻迭代间的地址步长参数说明请参考repeatStride；同一迭代内DataBlock的地址步长参数说明请参考dataBlockStride。

RoundMode取值说明

枚举类型具体定义为：

enum class RoundMode {
  CAST_NONE = 0,  // 在转换有精度损失时表示CAST_RINT模式，不涉及精度损失时表示不舍入
  CAST_RINT,      // rint，四舍六入五成双舍入
  CAST_FLOOR,     // floor，向负无穷舍入
  CAST_CEIL,      // ceil，向正无穷舍入
  CAST_ROUND,     // round，四舍五入舍入
  CAST_TRUNC,     // trunc，向零舍入
  CAST_ODD,       // Von Neumann rounding，最近邻奇数舍入
  CAST_HYBRID,    // hybrid，目前特指输出结果是hifloat8_t数据时，会用到的一种随机舍入
};

注：仅在支持hifloat8_t数据类型的硬件平台，才支持CAST_HYBRID转换模式。

数据类型

不同数据类型转换支持的舍入模式不同，可参考精度转换规则。

表 Ascend 950PR/Ascend 950DT支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
int4b_t int16_t CAST_NONE
int4b_t half CAST_NONE
int4b_t bfloat16_t CAST_NONE
int8_t int16_t CAST_NONE
int8_t half CAST_NONE
int8_t int32_t CAST_NONE
uint8_t uint16_t CAST_NONE
uint8_t half CAST_NONE
uint8_t uint32_t CAST_NONE
fp4x2_e2m1_t bfloat16_t CAST_NONE
fp4x2_e1m2_t bfloat16_t CAST_NONE
hifloat8_t half CAST_NONE
hifloat8_t float CAST_NONE
fp8_e5m2_t float CAST_NONE
fp8_e4m3fn_t float CAST_NONE
int16_t int4b_t CAST_NONE
int16_t uint8_t CAST_NONE
int16_t half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
int16_t int32_t CAST_NONE
int16_t uint32_t CAST_NONE
int16_t float CAST_NONE
uint16_t uint8_t CAST_NONE
uint16_t uint32_t CAST_NONE
half int4b_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half int8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half uint8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half hifloat8_t CAST_ROUND/CAST_HYBRID
half int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half bfloat16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half float CAST_NONE
bfloat16_t fp4x2_e2m1_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t fp4x2_e1m2_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t float CAST_NONE
int32_t uint8_t CAST_NONE
int32_t int16_t CAST_NONE
int32_t uint16_t CAST_NONE
int32_t half roundMode不生效，与SetDeqScale(half scale)接口配合使用。
int32_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
int32_t int64_t CAST_NONE
uint32_t uint8_t CAST_NONE
uint32_t int16_t CAST_NONE
uint32_t uint16_t CAST_NONE
float hifloat8_t CAST_ROUND/CAST_HYBRID
float fp8_e5m2_t CAST_RINT
float fp8_e4m3fn_t CAST_RINT
float int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_ODD/CAST_NONE
float bfloat16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int64_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
complex32 complex64 CAST_NONE
int64_t int32_t CAST_NONE
int64_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
int64_t double CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
double bfloat16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
double int32_t CAST_TRUNC
double float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
double int64_t CAST_TRUNC
complex64 complex32 CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_ODD/CAST_NONE
complex64 complex64 CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC

表 Atlas A3 训练系列产品/Atlas A3 推理系列产品支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
int4b_t half CAST_NONE
int8_t half CAST_NONE
uint8_t half CAST_NONE
int16_t half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
int16_t float CAST_NONE
half int4b_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half uint8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half float CAST_NONE
bfloat16_t int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t float CAST_NONE
int32_t int16_t CAST_NONE
int32_t half roundMode不生效，与SetDeqScale(half scale)接口配合使用。
int32_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
int32_t int64_t CAST_NONE
float int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_ODD/CAST_NONE
float bfloat16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int64_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
int64_t int32_t CAST_NONE
int64_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC

表 Atlas A2 训练系列产品/Atlas A2 推理系列产品支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
int4b_t half CAST_NONE
int8_t half CAST_NONE
uint8_t half CAST_NONE
int16_t half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
int16_t float CAST_NONE
half int4b_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half uint8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half float CAST_NONE
bfloat16_t int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t float CAST_NONE
int32_t int16_t CAST_NONE
int32_t half roundMode不生效，与SetDeqScale(half scale)接口配合使用。
int32_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
int32_t int64_t CAST_NONE
float int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_ODD/CAST_NONE
float bfloat16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int64_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
int64_t int32_t CAST_NONE
int64_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC

表 Atlas 200I/500 A2 推理产品支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
int8_t half CAST_NONE
uint8_t half CAST_NONE
int16_t half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
int16_t float CAST_NONE
half int8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half uint8_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half float CAST_NONE
bfloat16_t int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
bfloat16_t float CAST_NONE
int32_t int16_t CAST_NONE
int32_t half CAST_NONE
int32_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
int32_t int64_t CAST_NONE
float int16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float half CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_ODD/CAST_NONE
float bfloat16_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
float int64_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
int64_t int32_t CAST_NONE
int64_t float CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC

表 Atlas 推理系列产品AI Core支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
int8_t half CAST_NONE
uint8_t half CAST_NONE
int16_t half CAST_NONE
half int4b_t CAST_NONE
half int8_t CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half uint8_t CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int16_t CAST_RINT
half int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half float CAST_NONE
int32_t int16_t CAST_NONE
int32_t half roundMode不生效，与SetDeqScale(half scale)接口配合使用。
int32_t float CAST_NONE
float half CAST_ODD/CAST_NONE
float int64_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC

表 Atlas 训练系列产品支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
int8_t half CAST_NONE
uint8_t half CAST_NONE
int16_t half CAST_NONE
half int8_t CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half uint8_t CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC/CAST_NONE
half int32_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC
half float CAST_NONE
int32_t float CAST_NONE
float half CAST_ODD/CAST_NONE
float int64_t CAST_RINT/CAST_FLOOR/CAST_CEIL/CAST_ROUND/CAST_TRUNC

表 Kirin X90支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
half uint8_t CAST_CEIL
half int32_t CAST_FLOOR
int32_t float CAST_CEIL
float bfloat16_t CAST_ROUND
bfloat16_t float CAST_NONE

表 Kirin 9030支持的数据类型组合情况

src数据类型 dst数据类型 支持的roundMode
half uint8_t CAST_CEIL
half int32_t CAST_FLOOR
int32_t float CAST_CEIL
float bfloat16_t CAST_ROUND
bfloat16_t float CAST_NONE

返回值说明

无。

约束说明

  • 操作数地址对齐要求请参见通用地址对齐约束。

  • 操作数地址重叠约束请参考通用地址重叠约束。特别地，对于长度较小的数据类型转换为长度较大的数据类型时，地址重叠可能会导致结果错误。

  • 当源操作数和目的操作数位数不同时，计算输入参数以数据类型的字节较大的为准。例如，源操作数为half类型，目的操作数为int32_t类型时，每次迭代最多操作64个元素，为保证输出和输入是连续的，dstRepStride应设置为8，srcRepStride应设置为4。

  • 当dst或src为int4b_t时，由于一个int4b_t只占半个字节，故申请Tensor空间时，只需申请相同数量的int8_t数据空间的一半。host侧目前暂不支持int4b_t，故在申请int4b_t类型的tensor时，应先申请一个类型为int8_t的tensor，再用Reinterpretcast接口转化为int4b_t类型的tensor，接着调用Cast指令。

  • 当dst或src为int4b_t时，tensor高维切分计算接口的连续模式的mask与tensor前n个数据计算接口的count必须为偶数；对于tensor高维切分计算接口的逐bit模式，对应同一字节的相邻两个比特位的数值必须一致，即0-1位数值一致，2-3位数值一致，4-5位数值一致，以此类推。

  • int32_t到half数据类型的转换，设置舍入模式无效，需要与SetDeqScale接口配合使用。

  • 针对Ascend 950PR/Ascend 950DT，complex32/complex64/double数据类型仅支持tensor前n个数据计算接口。

调用示例

本样例中只展示Compute流程中的部分代码。本样例的srcLocal为half类型，dstLocal为int32_t类型，计算mask时以int32_t为准。

如果您需要运行样例代码，完整的调用样例请参考Cast样例。

  • tensor高维切分计算样例-mask连续模式。

    uint64_t mask = 256 / sizeof(int32_t);
// repeatTime = 8, 64 elements one repeat, 512 elements total
// dstBlkStride, srcBlkStride = 1, no gap between blocks in one repeat
// dstRepStride = 8, srcRepStride = 4, no gap between repeats
AscendC::Cast(dstLocal, srcLocal, AscendC::RoundMode::CAST_CEIL, mask, 8, { 1, 1, 8, 4 });

  • tensor高维切分计算样例-mask逐bit模式。

    uint64_t mask[2] = { 0, UINT64_MAX };
// repeatTime = 8, 64 elements one repeat, 512 elements total
// dstBlkStride, srcBlkStride = 1, no gap between blocks in one repeat
// dstRepStride = 8, srcRepStride = 4, no gap between repeats
AscendC::Cast(dstLocal, srcLocal, AscendC::RoundMode::CAST_CEIL, mask, 8, { 1, 1, 8, 4 });

  • tensor前n个数据计算样例。

    uint32_t count = 512; // 参与计算的元素个数
AscendC::Cast(dstLocal, srcLocal, AscendC::RoundMode::CAST_CEIL, count);

根据不同的RoundMode取值，输出结果会有差异，下面样例以RoundMode::CAST_CEIL（向正无穷舍入）为例。

结果示例如下：

输入数据(srcLocal): 
[1.4, 1.5, 1.6, 2.4, 2.5, 2.6, ... 2.6]
输出数据(dstLocal): 
[2, 2, 2, 3, 3, 3, ... 3]

当RoundMode为RoundMode::CAST_NONE（half转int32_t有精度损失，此时同CAST_RINT模式）或RoundMode::CAST_RINT（四舍六入五成双舍入）时，结果示例如下：

输入数据(srcLocal): 
[1.4, 1.5, 1.6, 2.4, 2.5, 2.6, ... 2.6]
输出数据(dstLocal): 
[1, 2, 2, 2, 2, 3, ... 3]

当RoundMode为RoundMode::CAST_FLOOR（向负无穷舍入）时，结果示例如下：

输入数据(srcLocal): 
[1.4, 1.5, 1.6, 2.4, 2.5, 2.6, ... 2.6]
输出数据(dstLocal): 
[1, 1, 1, 2, 2, 2, ... 2]

当RoundMode为RoundMode::CAST_ROUND（四舍五入舍入）时，结果示例如下：

输入数据(srcLocal): 
[1.4, 1.5, 1.6, 2.4, 2.5, 2.6, ... 2.6]
输出数据(dstLocal): 
[1, 2, 2, 2, 3, 3, ... 3]

当RoundMode为RoundMode::CAST_TRUNC（向零舍入）时，结果示例如下：

输入数据(srcLocal): 
[1.4, 1.5, 1.6, 2.4, 2.5, 2.6, ... 2.6]
输出数据(dstLocal): 
[1, 1, 1, 2, 2, 2, ... 2]

  • 当Cast涉及int4b_t时，调用示例如下：

    dstLocal为int8_t类型，srcLocal为half类型。

    inBufferSize_ = srcSize;  // src buffer size
outBufferSize_ = srcSize / 2;   //dst buffer size
uint64_t mask = 128;
AscendC::LocalTensor<half> srcLocal;
srcLocal.SetSize(inBufferSize_);
AscendC::LocalTensor<int8_t> dstLocal;
dstLocal.SetSize(outBufferSize_);
AscendC::LocalTensor<AscendC::int4b_t> dstLocalTmp = dstLocal.ReinterpretCast<AscendC::int4b_t>();
// repeatTime = 1, 128 elements one repeat, 128 elements total
// dstBlkStride, srcBlkStride = 1, no gap between blocks in one repeat
// dstRepStride = 2, srcRepStride = 8, no gap between repeats
AscendC::Cast<AscendC::int4b_t, half>(dstLocalTmp, srcLocal, AscendC::RoundMode::CAST_CEIL, mask, 1, {1, 1, 2, 8});

更多样例

您可以参考以下样例，了解如何使用Cast指令的tensor高维切分计算接口，进行更灵活的操作、实现更高级的功能。

通过tensor高维切分计算接口中的mask连续模式，实现数据非连续计算。

uint64_t mask = 32;  // 每个迭代内只计算前32个数
AscendC::Cast(dstLocal, srcLocal, AscendC::RoundMode::CAST_CEIL, mask, 8, { 1, 1, 8, 4 });

结果示例如下，输出数据中每个迭代的前32个数以外的数据是未初始化数据：

输入数据(srcLocal): 
[37.4     7.11   53.5    19.44   22.66   43.     43.16    5.316  74.2
 15.7    87.75   86.94   92.56   25.45   36.06   94.6    73.6    30.48
 48.16   12.55   27.81   14.67    6.58   48.38   67.5    57.5    63.3
 85.2     3.654  68.7    52.53   16.38   13.945  63.84   87.2    82.5
 85.7    27.78   15.41   41.66   31.38   14.65   88.25    0.0332 43.06
 46.88   15.57   87.1    53.16   33.5    91.06   36.5    55.34   60.53
  3.238  23.92   97.5    91.1    78.44   54.47   82.     53.8    72.1
 25.06   32.12   15.88   33.38   36.7    33.3    84.4    19.25    1.743
 46.16   22.06    4.582  71.1    15.94   22.23   53.47   17.05   48.56
 94.44   77.4    90.2    46.56   92.4     9.45   68.44   35.7    31.62
 68.1    63.7    77.     92.06   20.45   27.67   93.4    22.39   17.22
 73.06    7.12   25.34   36.34   13.54   38.12   24.56   86.56   69.7
 68.3    30.38   68.4    86.1    54.44   70.     55.3    48.6    59.03
 64.44   15.45   66.5    92.7    60.7    52.22   47.     99.75   41.94
 43.06   89.5    36.9    62.5     1.306  48.06    9.37   62.25   20.61
 43.8    69.25   27.22   71.44   52.75   11.82   80.6    63.44   53.22
 85.44   25.25    2.309  26.88   84.5    29.83    9.93   81.9    97.75
 75.75   97.7    72.     19.86   26.62   88.7    74.06    9.24   42.5
 14.      39.44   98.56   66.94   89.     57.12   39.     11.57   19.05
 86.56   32.66   19.25   99.3    95.6    58.7    79.6    37.38   65.
 75.7     8.586  77.7     2.68   75.7    77.56   39.1    39.72   64.06
 98.44   30.27   31.9    94.4    85.94    4.965   2.758  92.4    49.53
 50.75    5.7    19.69   87.6    20.08   88.8    87.4    63.6    68.3
 78.9    45.66   10.01   35.25   71.9    37.38   39.7    43.47   11.67
 64.3    35.62   74.3    59.3    28.69   29.56   23.14   36.22    4.88
 70.5    25.05   72.6    71.6    32.28   34.66   80.     96.1    98.7
 12.91   95.4    61.97   87.94   19.1    40.47   89.6    84.     29.72
 17.8    81.44   23.25   33.03   18.67   78.     49.62   63.1    72.75
 77.25    3.74   38.9    17.92   76.     25.62   34.53   84.     32.03
 57.3     9.21    6.836  68.9    35.78   96.75   56.3    96.1    23.45
 78.75   94.25   12.44   56.7    24.55   25.11   90.7    50.94   78.4
  3.576  21.81   53.28   26.2    43.1     7.742  13.4    86.44   86.9
 13.93   16.48   91.06   42.3    95.5    66.8    40.6    98.06   71.9
 67.6    55.9    82.44   93.75   41.53   23.62   40.12   40.53   80.7
 80.25   96.3    51.38   93.6    91.3    32.84   88.     69.7    63.16
 41.75   43.22   43.22   31.73   84.9    91.6    80.     53.34   27.12
 76.6    97.25   44.5    30.28   74.3    76.06   40.     41.28   37.72
 99.56   18.73   16.45   92.75   79.1    40.3    68.     23.98   88.7
 86.6    24.97   59.6    28.25   82.94   46.12   60.12   34.53   79.7
 11.086  20.25   44.88   39.97   42.12   62.7    30.66   42.56   16.69
 85.2    90.8    78.75   26.16   18.14   94.06   40.3    20.16   38.
 12.99   95.44   76.25   26.03   76.     30.06   27.25   84.56   30.45
 66.1    83.25    3.732  39.1    54.22   82.8    43.22   53.03   11.66
 88.1     6.83   66.8    44.4     7.5    24.77   74.4    35.9    79.75
 41.62   37.06   60.12   57.9    96.94   84.25   39.88   22.55   72.7
 58.9    44.75   90.4    46.34   71.3    16.4    26.12   21.45   10.27
 2.      41.53   39.03   80.25    2.11    7.88   72.2    27.83   88.1
 67.56   10.72   52.84   91.2    97.6    51.44   74.7     3.527  79.25
 11.3    19.16   39.53    3.469  98.7    45.72   40.16   47.1    71.8
 11.81   52.97   71.44   37.7    26.81   46.22   26.94    4.805  12.18
 70.4    51.4    24.2    83.9     9.62   12.445  57.6    85.8    55.12
 88.25   32.38   62.88    1.903  47.72   35.9    48.94   86.06   32.44
  1.219  35.56   49.78   49.97   24.45   94.5    99.94   44.72    3.404
 83.6    23.14   76.7    91.7    24.33   20.62   24.72    4.55   88.94
 87.44   95.75   41.56   13.77   34.6    95.94   77.1    24.28   70.06
 10.06   11.38   88.8    57.22   94.56   35.     79.8    58.22   44.06
 26.9    16.25   99.94   51.1    42.38   84.25    0.9604 48.1   ]

输出数据(dstLocal): 
[        38          8         54         20         23         43
         44          6         75         16         88         87
         93         26         37         95         74         31
         49         13         28         15          7         49
         68         58         64         86          4         69
         53         17 1879993057 1827499998 1823960025 1570990114
 1828150463 1811639312 1794470101 1754296176 1888841335 1715628997
 1839753994 1850888497 1889364175 1891068936 1823369913 1769105534
 1815638091 1808559970 1601662785 1739089473 1863146361 1694785989
 1597138938 1836478181 1888774249 1637707434 1877372650 1796304934
 1887530885 1839295471 1707240971 1873242695         33         16
         34         37         34         85         20          2
         47         23          5         72         16         23
         54         18         49         95         78         91
         47         93         10         69         36         32
         69         64         77         93         21         28
 1753837732 1488743807 1711632378 1799581711 1818783215 1891790695
 1837723802 1752132873 1727950918 1760390205 1866887130 1824876865
 1807839436 1890544910 1889755550 1787129270 1502702106 1841065201
 1820156583 1779396288 1760521448 1844604520 1831039103 1843491014
 1891199259 1839493317 1801349958 1577807434 1811377215 1879404734
 1826057367 1837853054         37         63          2         49
         10         63         21         44         70         28
         72         53         12         81         64         54
         86         26          3         27         85         30
         10         82         98         76         98         72
         20         27         89         75 1890086927 1826517134
 1814783944 1824156809 1875733079 1842114682 1845456975 1830120794
 1787980861 1807380585 1535469972 1883860884 1889167601 1747872128
 1888317235 1720937006 1836806331 1654152236 1695309475 1892773593
 1840737395 1868392748 1833724316 1600153936 1869310159 1883467778
 1892641857 1776248953 1833201514 1886743848 1745972258 1860657622
         95         86          5          3         93         50
         51          6         20         88         21         89
         88         64         69         79         46         11
         36         72         38         40         44         12
         65         36         75         60         29         30
         24         37 1733652589 1756325317 1685744372 1780772214
 1660252348 1629973784 1847815925 1828941229 1683778661 1519480967
 1762160488 1844801381 1832742021 1891724641 1761701480 1695312651
 1429433841 1774275423 1828349211 1779786303 1835953259 1784896595
 1858432988 1413442268 1893363867 1886679050 1872588913 1473866635
 1793158916 1762946052 1719627087 1893231666         76         26
         35         84         33         58         10          7
         69         36         97         57         97         24
         79         95         13         57         25         26
         91         51         79          4         22         54
         27         44          8         14         87         87
 1208960410 1208567888 1215973275 1214859418 1210992732 1208305714
 1165379704 1215252623 1197033625 1212172318 1217415148 1211058280
 1206798479 1215645776 1209223143 1217611809 1212500082 1215383615
 1208567769 1200179260 1216694386 1218070398 1195526187 1211516213
 1213089850 1213941743 1216825148 1212565573 1216694248 1217546087
 1200178778 1215973524         92         80         54         28
         77         98         45         31         75         77
         40         42         38        100         19         17
         93         80         41         68         24         89
         87         25         60         29         83         47
         61         35         80         12 1189759014 1218070662
 1211057953 1216825400 1215121414 1214596952 1216169420 1210075102
 1209157633 1213941279 1195984973 1211648118 1201686666 1212041360
 1216103688 1212500024 1173112887 1194608648 1216825427 1209747582
 1207191587 1214859224 1203980407 1215711379 1213155353 1203259396
 1214859350 1211779156 1217218713 1202473003 1216628529 1196771437
         44         54         12         89          7         67
         45          8         25         75         36         80
         42         38         61         58         97         85
         40         23         73         59         45         91
         47         72         17         27         22         11
         91         42 1211516990 1198213215 1203848735 1217349746
 1212303398 1217808150 1215514752 1209878647 1214138433 1215711277
 1212041273 1215383541 1214728294 1197754500 1169574019 1208371214
 1214269569 1216301092 1216563283 1213548677 1217873970 1203128459
 1209812695 1218136029 1194805359 1204439186 1218005120 1213941626
 1217153046 1208109091 1215055928 1215318166          5         13
         71         52         25         84         10         13
         58         86         56         89         33         63
          2         48         36         49         87         33
          2         36         50         50         25         95
        100         45          4         84         24         77
 1202210969 1213679767 1209288847 1217480263 1184319410 1214072674
 1188382819 1217283870 1200769107 1217939416 1199327294 1213351841
 1206667407 1217153163 1215580283 1214138474 1206798167 1194477696
 1193690499 1214072706 1216825421 1216693888 1217611496 1198540949
 1199654414 1206405188 1214203847 1165183076 1213745302 1208830102
 1209944118 1215121459]