#include "kernel_operator.h"
using namespace AscendC;
constexpr int32_t BUFFER_NUM = 2;
constexpr int32_t TOTAL_LENGTH = 8 * 200 * 1024;
constexpr int32_t TILE_NUM = 32;
constexpr float alpha = 0.002;
class KernelPRelu {
public:
__aicore__ inline KernelPRelu() {}
__aicore__ inline void Init(GM_ADDR x, GM_ADDR y, uint32_t totalLength, uint32_t tileNum, float alpha)
{
PRINTF("[npu debug] >>> GetBlockNum() %d", GetBlockNum());
ASSERT(GetBlockNum() != 0 && "block dim can not be zero!");
this->blockLength = totalLength / GetBlockNum();
this->tileNum = tileNum;
this->alpha = static_cast<float>(alpha);
ASSERT(tileNum != 0 && "tile num can not be zero!");
this->tileLength = this->blockLength / tileNum / BUFFER_NUM;
// get start index for current core, core parallel
xGm.SetGlobalBuffer((__gm__ float*)x + this->blockLength * GetBlockIdx(), this->blockLength);
yGm.SetGlobalBuffer((__gm__ float*)y + this->blockLength * GetBlockIdx(), this->blockLength);
// pipe alloc memory to queue, the unit is Bytes
pipe.InitBuffer(inQueueX, BUFFER_NUM, this->tileLength * sizeof(float));
pipe.InitBuffer(outQueueY, BUFFER_NUM, this->tileLength * sizeof(float));
pipe.InitBuffer(tmpBuffer1, this->tileLength * sizeof(float));
//pipe.InitBuffer(tmpBuffer2, this->tileLength * sizeof(float));
}
__aicore__ inline void Process()
{
// loop count need to be doubled, due to double buffer
int32_t loopCount = this->tileNum * BUFFER_NUM;
// tiling strategy, pipeline parallel
for (int32_t i = 0; i < loopCount; i++) {
CopyIn(i);
Compute(i);
CopyOut(i);
}
}
private:
__aicore__ inline void CopyIn(int32_t progress)
{
// alloc tensor from queue memory
LocalTensor<float> xLocal = inQueueX.AllocTensor<float>();
// copy progress_th tile from global tensor to local tensor
DataCopy(xLocal, xGm[progress * this->tileLength], this->tileLength);
// enque input tensors to VECIN queue
inQueueX.EnQue(xLocal);
}
__aicore__ inline void Compute(int32_t progress)
{
// deque input tensors from VECIN queue
LocalTensor<float> xLocal = inQueueX.DeQue<float>();
LocalTensor<float> yLocal = outQueueY.AllocTensor<float>();
LocalTensor<float> tmpTensor1 = tmpBuffer1.Get<float>();
float inputVal = 0.0;
Maxs(tmpTensor1, xLocal, inputVal, this->tileLength); // x >= 0 --> x
// x < 0
Mins(xLocal, xLocal, inputVal, this->tileLength);
Muls(xLocal, xLocal, this->alpha, this->tileLength);
Add(yLocal, xLocal, tmpTensor1, this->tileLength);
outQueueY.EnQue<float>(yLocal);
// free input tensors for reuse
inQueueX.FreeTensor(xLocal);
}
__aicore__ inline void CopyOut(int32_t progress)
{
// deque output tensor from VECOUT queue
LocalTensor<float> yLocal = outQueueY.DeQue<float>();
// copy progress_th tile from local tensor to global tensor
DataCopy(yGm[progress * this->tileLength], yLocal, this->tileLength);
// free output tensor for reuse
outQueueY.FreeTensor(yLocal);
}
private:
TPipe pipe;
TBuf<QuePosition::VECCALC> tmpBuffer1;
//TBuf<QuePosition::VECCALC> tmpBuffer1, tmpBuffer2;
// create queues 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<float> xGm, yGm;
uint32_t blockLength;
uint32_t tileNum;
uint32_t tileLength;
float alpha;
};
extern "C" __global__ __aicore__ void p_relu_custom(GM_ADDR x, GM_ADDR y) {
//GET_TILING_DATA(tiling_data, tiling);
// TODO: user kernel impl
KernelPRelu op;
op.Init(x, y, TOTAL_LENGTH, TILE_NUM, alpha);
op.Process();
}
#ifndef __CCE_KT_TEST__
// call of kernel function
void p_relu_custom_do(uint32_t blockDim, void* l2ctrl, void* stream, uint8_t* x, uint8_t* y)
{
p_relu_custom<<<blockDim, l2ctrl, stream>>>(x, y);
}
#endif
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