实现样例
Ascend C Add算子实现文件:add_custom.cpp
/* * Copyright (c) Huawei Technologies Co., Ltd. 2022-2023. All rights reserved. * * Function : z = x + y * This sample is a very basic sample that implements vector add on Ascend plaform. * In this sample: * Length of x / y / z is 8*2048. * Num of vector core used in sample is 8. * Length for each core to compute is 2048. * Tiles for each core is 8 which means we add 2048/8=256 elements in one loop. * * This is just a tile strategy for demonstration, in fact we can compute at most 128*255 * elements in one loop for b16 type. */ #include "kernel_operator.h" using namespace AscendC; constexpr int32_t TOTAL_LENGTH = 8 * 2048; // total length of data constexpr int32_t USE_CORE_NUM = 8; // num of core used constexpr int32_t BLOCK_LENGTH = TOTAL_LENGTH / USE_CORE_NUM; // length computed of each core constexpr int32_t TILE_NUM = 8; // split data into 8 tiles for each core constexpr int32_t BUFFER_NUM = 2; // tensor num for each queue constexpr int32_t TILE_LENGTH = BLOCK_LENGTH / TILE_NUM / BUFFER_NUM; // seperate to 2 parts, due to double buffer class KernelAdd { public: __aicore__ inline KernelAdd() {} __aicore__ inline void Init(GM_ADDR x, GM_ADDR y, GM_ADDR z) { // get start index for current core, core parallel xGm.SetGlobalBuffer((__gm__ half*)x + BLOCK_LENGTH * GetBlockIdx(), BLOCK_LENGTH); yGm.SetGlobalBuffer((__gm__ half*)y + BLOCK_LENGTH * GetBlockIdx(), BLOCK_LENGTH); zGm.SetGlobalBuffer((__gm__ half*)z + BLOCK_LENGTH * GetBlockIdx(), BLOCK_LENGTH); // pipe alloc memory to queue, the unit is Bytes pipe.InitBuffer(inQueueX, BUFFER_NUM, TILE_LENGTH * sizeof(half)); pipe.InitBuffer(inQueueY, BUFFER_NUM, TILE_LENGTH * sizeof(half)); pipe.InitBuffer(outQueueZ, BUFFER_NUM, TILE_LENGTH * sizeof(half)); } __aicore__ inline void Process() { // loop count need to be doubled, due to double buffer constexpr int32_t loopCount = TILE_NUM * 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<half> xLocal = inQueueX.AllocTensor<half>(); LocalTensor<half> yLocal = inQueueY.AllocTensor<half>(); // copy progress_th tile from global tensor to local tensor DataCopy(xLocal, xGm[progress * TILE_LENGTH], TILE_LENGTH); DataCopy(yLocal, yGm[progress * TILE_LENGTH], TILE_LENGTH); // enque input tensors to VECIN queue inQueueX.EnQue(xLocal); inQueueY.EnQue(yLocal); } __aicore__ inline void Compute(int32_t progress) { // deque input tensors from VECIN queue LocalTensor<half> xLocal = inQueueX.DeQue<half>(); LocalTensor<half> yLocal = inQueueY.DeQue<half>(); LocalTensor<half> zLocal = outQueueZ.AllocTensor<half>(); // call Add instr for computation Add(zLocal, xLocal, yLocal, TILE_LENGTH); // enque the output tensor to VECOUT queue outQueueZ.EnQue<half>(zLocal); // free input tensors for reuse inQueueX.FreeTensor(xLocal); inQueueY.FreeTensor(yLocal); } __aicore__ inline void CopyOut(int32_t progress) { // deque output tensor from VECOUT queue LocalTensor<half> zLocal = outQueueZ.DeQue<half>(); // copy progress_th tile from local tensor to global tensor DataCopy(zGm[progress * TILE_LENGTH], zLocal, TILE_LENGTH); // free output tensor for reuse outQueueZ.FreeTensor(zLocal); } private: TPipe pipe; // create queues for input, in this case depth is equal to buffer num TQue<QuePosition::VECIN, BUFFER_NUM> inQueueX, inQueueY; // create queue for output, in this case depth is equal to buffer num TQue<QuePosition::VECOUT, BUFFER_NUM> outQueueZ; GlobalTensor<half> xGm, yGm, zGm; }; // implementation of kernel function extern "C" __global__ __aicore__ void add_custom(GM_ADDR x, GM_ADDR y, GM_ADDR z) { KernelAdd op; op.Init(x, y, z); op.Process(); } #ifndef __CCE_KT_TEST__ // call of kernel function void add_custom_do(uint32_t blockDim, void* l2ctrl, void* stream, uint8_t* x, uint8_t* y, uint8_t* z) { add_custom<<<blockDim, l2ctrl, stream>>>(x, y, z); } #endif
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