华为Ascend C算子开发（中级）考试题

提示：这个是河北廊坊Ascend C算子开发考试题和答案，仅供参考，因为不确定其他城市的考试题是否也是一样

一、op_host文件夹下的sinh_custom_tiling.h文件

#include "register/tilingdata_base.h"
namespace optiling {
BEGIN_TILING_DATA_DEF(SinhCustomTilingData)
 //考生自行定义 tiling 结构体成员变量
TILING_DATA_FIELD_DEF(uint32_t, totalLength);
TILING_DATA_FIELD_DEF(uint32_t, tileNum);
END_TILING_DATA_DEF;
REGISTER_TILING_DATA_CLASS(SinhCustom, SinhCustomTilingData)
}

二、op_host文件夹下的sinh_custom.cpp文件

#include "sinh_custom_tiling.h"
#include "register/op_def_registry.h"
namespace optiling {
static ge::graphStatus TilingFunc(gert::TilingContext* context)
{
 SinhCustomTilingData tiling;
 //考生自行填充
 const uint32_t BLOCK_DIM = 8;
 const uint32_t TILE_NUM = 8;
 uint32_t totalLength = context->GetInputShape(0)->GetOriginShape().GetShapeSize();
 context->SetBlockDim(BLOCK_DIM);
 tiling.set_totalLength(totalLength);
 tiling.set_tileNum(TILE_NUM);
 tiling.SaveToBuffer(context->GetRawTilingData()->GetData(), 
context->GetRawTilingData()->GetCapacity());
 context->GetRawTilingData()->SetDataSize(tiling.GetDataSize());
 size_t *currentWorkspace = context->GetWorkspaceSizes(1);
 currentWorkspace[0] = 0;
 return ge::GRAPH_SUCCESS;
}
}
namespace ge {
static ge::graphStatus InferShape(gert::InferShapeContext* context)
{
 const gert::Shape* x1_shape = context->GetInputShape(0);
 gert::Shape* y_shape = context->GetOutputShape(0);
 *y_shape = *x1_shape;
 return GRAPH_SUCCESS;
}
}
namespace ops {
class SinhCustom : public OpDef {
public:
 explicit SinhCustom(const char* name) : OpDef(name)
 {
 this->Input("x")
 .ParamType(REQUIRED)
 .DataType({ge::DT_FLOAT16})
 .Format({ge::FORMAT_ND})
 .UnknownShapeFormat({ge::FORMAT_ND});
 this->Output("y")
 .ParamType(REQUIRED)
 .DataType({ge::DT_FLOAT16})
 .Format({ge::FORMAT_ND})
 .UnknownShapeFormat({ge::FORMAT_ND});
 this->SetInferShape(ge::InferShape);
 this->AICore()
 .SetTiling(optiling::TilingFunc);
 this->AICore().AddConfig("ascend310b");
 }
};
OP_ADD(SinhCustom);
}

三、op_kernel文件夹下的sinh_custom.cpp文件

#include "kernel_operator.h"
using namespace AscendC;
constexpr int32_t BUFFER_NUM = 2;
class KernelSinh {
public:
 __aicore__ inline KernelSinh() {}
 __aicore__ inline void Init(GM_ADDR x, GM_ADDR y, uint32_t totalLength, uint32_t 
tileNum)
 {
 //考生补充初始化代码
 ASSERT(GetBlockNum() != 0 && "block dim can not be zero!");
 this->blockLength = totalLength / GetBlockNum();
 this->tileNum = tileNum;
 ASSERT(tileNum != 0 && "tile num can not be zero!");
 this->tileLength = this->blockLength / tileNum / BUFFER_NUM;
 xGm.SetGlobalBuffer((__gm__ DTYPE_X *)x + this->blockLength * GetBlockIdx(), 
this->blockLength);
 yGm.SetGlobalBuffer((__gm__ DTYPE_Y *)y + this->blockLength * GetBlockIdx(), 
this->blockLength);
 pipe.InitBuffer(inQueueX, BUFFER_NUM, this->tileLength * sizeof(DTYPE_X));
 pipe.InitBuffer(outQueueY, BUFFER_NUM, this->tileLength * sizeof(DTYPE_Y));
 pipe.InitBuffer(tmpBuffer1, this->tileLength * sizeof(DTYPE_X));
 pipe.InitBuffer(tmpBuffer2, this->tileLength * sizeof(DTYPE_X));
 pipe.InitBuffer(tmpBuffer3, this->tileLength * sizeof(DTYPE_X));
 pipe.InitBuffer(tmpBuffer4, this->tileLength * sizeof(DTYPE_X));
 }
 __aicore__ inline void Process()
 {
 //考生补充对“loopCount”的定义，注意对 Tiling 的处理
 int32_t loopCount = this->tileNum * BUFFER_NUM;
 for (int32_t i = 0; i < loopCount; i++) {
 CopyIn(i);
 Compute(i);
 CopyOut(i);
 }
 }
private:
 __aicore__ inline void CopyIn(int32_t progress)
 {
 //考生补充算子代码
 LocalTensor<DTYPE_X> xLocal = inQueueX.AllocTensor<DTYPE_X>();
 DataCopy(xLocal, xGm[progress * this->tileLength], this->tileLength);
 inQueueX.EnQue(xLocal);
 }
 __aicore__ inline void Compute(int32_t progress)
 {
 //考生补充算子计算代码
 LocalTensor<DTYPE_X> xLocal = inQueueX.DeQue<DTYPE_X>();
 LocalTensor<DTYPE_Y> yLocal = outQueueY.AllocTensor<DTYPE_Y>();
 LocalTensor<DTYPE_X> tmpTensor1 = tmpBuffer1.Get<DTYPE_X>();
 LocalTensor<DTYPE_X> tmpTensor2 = tmpBuffer2.Get<DTYPE_X>();
 LocalTensor<DTYPE_X> tmpTensor3 = tmpBuffer3.Get<DTYPE_X>();
 LocalTensor<DTYPE_X> tmpTensor4 = tmpBuffer4.Get<DTYPE_X>();
 DTYPE_X inputVal1 = -1;
 DTYPE_X inputVal2 = 0.5;
 //sinh(x) = (exp(x) - exp(-x)) / 2.0
 Muls(tmpTensor1, xLocal, inputVal1, this->tileLength);
 Exp(tmpTensor2, tmpTensor1, this->tileLength);
 Exp(tmpTensor3, xLocal, this->tileLength);
 Sub(tmpTensor4, tmpTensor3, tmpTensor2, this->tileLength);
 Muls(yLocal, tmpTensor4, inputVal2, this->tileLength);
 outQueueY.EnQue<DTYPE_Y>(yLocal);
 inQueueX.FreeTensor(xLocal);
 }
 __aicore__ inline void CopyOut(int32_t progress)
 {
 //考生补充算子代码
 LocalTensor<DTYPE_Y> yLocal = outQueueY.DeQue<DTYPE_Y>();
 DataCopy(yGm[progress * this->tileLength], yLocal, this->tileLength);
 outQueueY.FreeTensor(yLocal);
 }
private:
 TPipe pipe;
 //create queue for input, in this case depth is equal to buffer num
 TQue<QuePosition::VECIN, BUFFER_NUM> inQueueX;
 //create queue for output, in this case depth is equal to buffer num
 TQue<QuePosition::VECOUT, BUFFER_NUM> outQueueY;
 GlobalTensor<half> xGm;
 GlobalTensor<half> yGm;
 //考生补充自定义成员变量
 TBuf<QuePosition::VECCALC> tmpBuffer1, tmpBuffer2, tmpBuffer3, tmpBuffer4;
 uint32_t blockLength;
 uint32_t tileNum;
 uint32_t tileLength;
};
extern "C" __global__ __aicore__ void sinh_custom(GM_ADDR x, GM_ADDR y, GM_ADDR 
workspace, GM_ADDR tiling) {
 GET_TILING_DATA(tiling_data, tiling);
 KernelSinh op;
 //补充 init 和 process 函数调用内容
 op.Init(x, y, tiling_data.totalLength, tiling_data.tileNum);
 op.Process();
}