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#include "kernel_operator.h"
// optimize me. Use template to avoid copy code.
using namespace AscendC;
#define BUFFER_NUM 2
#define QK8_0 32
class GET_ROW_Q8_0 {
public:
__aicore__ inline GET_ROW_Q8_0() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, int64_t *indices_ne_ub,
size_t *indices_nb_ub, int64_t *output_ne_ub,
size_t *output_nb_ub) {
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
indices_ne[i] = indices_ne_ub[i];
indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
scale_ne[i] = input_ne_ub[i];
output_ne[i] = output_ne_ub[i];
output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
}
// one scale for a group.
scale_ne[0] /= QK8_0;
input_stride[0] = 1;
scale_stride[0] = 1;
output_stride[0] = 1;
for (int i = 1; i < 4; i++) {
input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
}
group_size_in_row = input_ne[0] / QK8_0;
int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
input_ne[3] * sizeof(int8_t);
// Indices has two dims. n_elements = all rows should get.
// dr, all rows should this thread get.
uint64_t n_elements =
indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
dr = n_elements / op_block_num;
uint64_t tails = n_elements % op_block_num;
if (op_block_idx < tails) {
dr += 1;
ir = dr * op_block_idx;
} else {
ir = dr * op_block_idx + tails;
}
input_gm.SetGlobalBuffer((__gm__ int8_t *)input);
scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
pipe.InitBuffer(cast_queue, BUFFER_NUM, QK8_0 * sizeof(half));
pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(float));
}
__aicore__ inline void copy_in(uint32_t offset) {
LocalTensor<int8_t> input_local = input_queue.AllocTensor<int8_t>();
DataCopy(input_local, input_gm[offset], QK8_0);
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
DataCopy(output_gm[offset], output_local, QK8_0);
output_queue.FreeTensor(output_local);
}
__aicore__ inline void calculate_group(int64_t idx, int64_t group) {
const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
const int64_t indices_ne1_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
indices_ne[0];
const int64_t indices_ne0_idx =
(idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
indices_ne1_idx * indices_ne[0]);
const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
indices_ne1_idx * indices_stride[1] +
indices_ne2_idx * indices_stride[2];
const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
const int64_t input_offset = selected_row_idx * input_stride[1] +
indices_ne1_idx * input_stride[2] +
indices_ne2_idx * input_stride[3] +
group * QK8_0;
const int64_t scale_offset = selected_row_idx * scale_stride[1] +
indices_ne1_idx * scale_stride[2] +
indices_ne2_idx * scale_stride[3] + group;
const int64_t output_offset = indices_ne0_idx * output_stride[1] +
indices_ne1_idx * output_stride[2] +
indices_ne2_idx * output_stride[3] +
group * QK8_0;
copy_in(input_offset);
LocalTensor<int8_t> input_local = input_queue.DeQue<int8_t>();
LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
// TODO: cast more data to speed up.
Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
Cast(output_local, cast_local, RoundMode::CAST_NONE, QK8_0);
// Only mul need compile by group.
half scale = scale_gm.GetValue(scale_offset);
Muls(output_local, output_local, (float)scale, QK8_0);
input_queue.FreeTensor(input_local);
cast_queue.FreeTensor(cast_local);
output_queue.EnQue(output_local);
copy_out(output_offset);
}
__aicore__ inline void calculate() {
for (int64_t i = ir; i < ir + dr; i++) {
for (int64_t j = 0; j < group_size_in_row; j++) {
calculate_group(i, j);
}
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t scale_ne[4];
size_t scale_stride[4];
int64_t indices_ne[4];
size_t indices_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
int64_t ir;
int64_t dr;
int64_t group_size_in_row;
TPipe pipe;
GlobalTensor<int8_t> input_gm;
GlobalTensor<half> scale_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
};
template <typename T>
__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
auto gm_ptr = (__gm__ uint8_t *)gm;
auto ub_ptr = (uint8_t *)(ub);
for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
*ub_ptr = *gm_ptr;
}
}
extern "C" __global__ __aicore__ void ascendc_get_row_q8_0(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
int64_t input_ne_ub[4];
int64_t indices_ne_ub[4];
size_t indices_nb_ub[4];
int64_t output_ne_ub[4];
size_t output_nb_ub[4];
copy_to_ub(input_ne_gm, input_ne_ub, 32);
copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
GET_ROW_Q8_0 op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}
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