TdAI / llama.cpp /ggml /src /ggml-cpu /spacemit /rvv_kernels.h
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#pragma once
#include "ggml-cpu-impl.h"
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <functional>
namespace spacemit_kernels {
constexpr auto div_round_up(auto up, auto down) {
return (up + down - 1) / down;
}
// Q8 Blk [f32] [s16] [int8 * blk_len]
// Q8 Blk N [f32 * N] [s16 * N] [int8 * blk_len * N]
constexpr size_t q8_blk_size(size_t blk_len, bool with_blk_sum = false) {
const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t) + (with_blk_sum ? sizeof(int16_t) : 0);
return blk_size;
}
// Q8 HP row block: K is split into K32 subblocks.
// Each subblock stores [f32 scale] [int8 * 32], with an optional fp16 sum trailer per subblock.
constexpr size_t q8_hp_blk_size(size_t blk_len, bool with_blk_sum = false, bool with_blk_scale = false) {
const size_t subblk_count = div_round_up(blk_len, size_t(32));
const size_t blk_size = blk_len * sizeof(int8_t) + subblk_count * sizeof(_Float16) +
(with_blk_sum ? subblk_count * sizeof(_Float16) : 0) +
(with_blk_scale ? sizeof(_Float16) : 0);
return blk_size;
}
// Q8K Blk [f32] [s16 * (blk_len / 16)] [int8 * blk_len]
// Q8K Blk N [f32 * N] [s16 * (blk_len / 16) * N] [int8 * blk_len * N]
constexpr size_t q8k_blk_size(size_t blk_len) {
const size_t blk_size = sizeof(float) + blk_len * sizeof(int8_t) + sizeof(int16_t) * blk_len / 16;
return blk_size;
}
using quantize_a_row_def = std::function<void(size_t, const float *, size_t, uint8_t *)>;
namespace rvv {
void memcpy1d(void * dst, const void * src, int64_t size);
void memcpy2d(void * dst, int64_t dst_stride, const void * src, int64_t src_stride, int64_t tile_rows, int64_t size);
void forward_flash_attn_ext_f16_one_chunk_vlen1024_vf16(const ggml_compute_params * params,
ggml_tensor * dst,
int ir0,
int ir1,
void * tcm_buffer,
size_t tcm_buffer_size);
void forward_flash_attn_ext_f16_tiled_vlen1024_vf16(const ggml_compute_params * params,
ggml_tensor * dst,
int ir0,
int ir1,
void * tcm_buffer,
size_t tcm_buffer_size);
void forward_rms_norm_f32(ggml_compute_params * params, ggml_tensor * op);
void forward_norm_f32(ggml_compute_params * params, ggml_tensor * op);
void forward_cont_with_permute(ggml_compute_params * params, ggml_tensor * op);
void forward_cpy_with_permute(ggml_compute_params * params, ggml_tensor * op);
template <typename T> void forward_get_rows(ggml_compute_params * params, ggml_tensor * op);
template <typename T> void forward_concat(ggml_compute_params * params, ggml_tensor * op);
template <ggml_op op_type, typename T> void forward_binary(ggml_compute_params * params, ggml_tensor * op);
template <typename T> void forward_sum_rows(const ggml_compute_params * params, ggml_tensor * op);
template <typename T> void forward_repeat_nrows(ggml_compute_params * params, ggml_tensor * op);
template <typename T> void forward_repeat_dim1(ggml_compute_params * params, ggml_tensor * op);
void quantize_a_row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
void quantize_a_4row_i8(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
void quantize_a_row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
void quantize_a_4row_i8_hp(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
void quantize_a_row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
void quantize_a_4row_i8k(size_t blk_len, const float * a_ptr, size_t count_k, uint8_t * quant_a_ptr);
} // namespace rvv
} // namespace spacemit_kernels