示例 - Reduce¶
功能说明¶
根据 ReduceOp 中定义的计算规则对最高维度进行规约操作,例如输入为 x[N, H, W, C], axis 取值为 0, 规约后为 out[1, H, W, C],此处以 ReduceSum 为例进行介绍。
ReduceOp 定义¶
template <typename Tx, typename Ty = Tx>
struct IdentityFunctor {
HOSTDEVICE explicit inline IdentityFunctor(int n) {}
HOSTDEVICE inline Ty operator()(const Tx& x) const {
return static_cast<Ty>(x);
}
};
template <typename Tx, typename Ty = Tx>
struct AddFunctor {
inline Ty initial() { return static_cast<Ty>(0.0f); }
__device__ __forceinline__ Ty operator()(const Ty &a, const Ty &b) const {
return b + a;
}
};
kernel 实现说明¶
对最高维进行规约操作,将不需要进行规约的维度进行合并,根据 NX 和 blockDim.x 对 H * W * C 进行 block 划分。对于 blockIdx_1,数据个数小于 blockDim.x * NX,则设置 IsBoundary = true,避免访存越界。将数据从全局内存中读取到寄存器中,每个线程读取 4 个元素,线程间数据没有依赖,进行线程内规约操作得到最终结果。将数据从寄存器写入全局内存中。 ReduceSum 数据处理过程如下:
kernel 代码¶
template <typename Tx, typename Ty, typename MPType, typename ReduceOp, typename TransformOp, bool IsBoundary = false>
__device__ void HigherDimImpl(const Tx* x, Ty* y, ReduceOp reducer,
TransformOp transform, MPType init,
int reduce_num, int left_num,
int block_num) {
const int NY = 2;
int idx = blockIdx.x * blockDim.x;
int idy = blockIdx.y * block_num; // block_offset of rows
Tx reduce_input[NY];
MPType reduce_compute[NY];
MPType result = init;
int block_offset = idy * left_num + idx + blockIdx.z * reduce_num * left_num; // the offset of this block
int store_offset = blockIdx.y * left_num + blockIdx.z * gridDim.y * left_num + idx;
const Tx* input = x + block_offset;
// how many columns left
int num = left_num - idx;
// how many rows have to be reduced
int loop = reduce_num - idy;
loop = loop > block_num ? block_size : loop;
for (int loop_index = 0; loop_index < loop; loop_index += NY) {
kps::ReadData<Tx, Tx, 1, NY, 1, IsBoundary>(&reduce_input[0], input + loop_index * left_num, num, NY, 1, left_num);
kps::ElementwiseUnary<Tx, MPType, REDUCE_VEC_num, 1, 1, TransformOp>(&reduce_compute[0], &reduce_input[0], transform);
kps::Reduce<MPType, NY, 1, 1, ReduceOp, kps::details::ReduceMode::kLocalMode>( &result, &reduce_compute[0], reducer, false);
}
Ty temp_data = static_cast<Ty>(result);
kps::WriteData<Ty, 1, 1, 1, IsBoundary>(y + store_offset, &temp_data, num);
}
template <typename Tx, typename Ty, typename MPType, typename ReduceOp, typename TransformOp>
__global__ void ReduceHigherDimKernel(const Tx* x, Ty* y, ReduceOp reducer,
TransformOp transform, MPType init,
int reduce_num, int left_num,
int blocking_num) {
// get the remaining data of this kernel
int num = left_num - blockIdx.x * blockDim.x;
if (num >= blockDim.x) {
// The remaining data is larger than blockdim.x
HigherDimImpl<Tx, Ty, MPType, AddFunctor<Tx, Ty>, IdentityFunctor<Tx, Ty>, false>(
x, y, AddFunctor<Tx, Ty>(), IdentityFunctor<Tx, Ty>(), init, reduce_num, left_num, blocking_num);
} else {
// The remaining data is smaller than blockdim.x, IsBounary must be true
HigherDimImpl<Tx, Ty, MPType, AddFunctor<Tx, Ty>, IdentityFunctor<Tx, Ty>, true>(
x, y, AddFunctor<Tx, Ty>(), IdentityFunctor<Tx, Ty>(), init, reduce_num, left_num, blocking_num);
}
}