root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-11-11 13:30:35 +00:00
Code Issues Actions 11 Packages Projects Releases Wiki Activity

cann: merge get row operations of float type

Browse Source
This commit is contained in:
zhenweijin 2024-08-19 14:27:26 +08:00
parent cfac111e2b
commit 9373e2ba58
3 changed files with 51 additions and 201 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
ggml/src/ggml-cann/kernels/CMakeLists.txt
View File

@ -3,8 +3,7 @@ if (NOT SOC_TYPE)
endif()
file(GLOB SRC_FILES
get_row_f32.cpp
get_row_f16.cpp
get_row_float.cpp
get_row_q4_0.cpp
get_row_q8_0.cpp
quantize_f32_q8_0.cpp

180
ggml/src/ggml-cann/kernels/get_row_f32.cpp
View File

@ -1,180 +0,0 @@
#include "kernel_operator.h"
// optimize me. Use template to avoid copy code.
using namespace AscendC;
#define BUFFER_NUM 2
class GET_ROW_F32 {
public:
__aicore__ inline GET_ROW_F32() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_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];
input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
indices_ne[i] = indices_ne_ub[i];
indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
output_ne[i] = output_ne_ub[i];
output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
}
// 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__ float *)input);
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
uint64_t local_buffer_size = ((input_ne[0] * sizeof(float) + 31) & ~31);
local_buffer_elems = local_buffer_size / sizeof(float);
// TODO, consider long row that can't put in UB.
// All data should asign to 32. It's ok because all data is align to 32.
pipe.InitBuffer(input_queue, BUFFER_NUM, local_buffer_size);
pipe.InitBuffer(output_queue, BUFFER_NUM, local_buffer_size);
}
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
LocalTensor<float> input_local = input_queue.AllocTensor<float>();
size_t tail = len % 32;
len = len & ~31;
DataCopy(input_local, input_gm[offset], len);
if(tail != 0) {
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPadExtParams<float> padParams;
DataCopyPad(input_local[len], input_gm[offset + len],
dataCopyParams, padParams);
}
input_queue.EnQue(input_local);
}
__aicore__ inline void copy_out(uint32_t offset, size_t len) {
LocalTensor<float> output_local = output_queue.DeQue<float>();
size_t tail = len % 32;
len = len & ~31;
DataCopy(output_gm[offset], output_local, len);
if(tail != 0) {
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
}
output_queue.FreeTensor(output_local);
}
__aicore__ inline void calculate_row(int64_t idx) {
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];
const int64_t output_offset = indices_ne0_idx * output_stride[1] +
indices_ne1_idx * output_stride[2] +
indices_ne2_idx * output_stride[3];
copy_in(input_offset, input_ne[0]);
LocalTensor<float> input_local = input_queue.DeQue<float>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
DataCopy(output_local, input_local, local_buffer_elems);
output_queue.EnQue(output_local);
copy_out(output_offset, input_ne[0]);
input_queue.FreeTensor(input_local);
}
__aicore__ inline void calculate() {
for (int64_t i = ir; i < ir + dr; i++) {
calculate_row(i);
}
}
private:
int64_t input_ne[4];
size_t input_stride[4];
int64_t indices_ne[4];
size_t indices_stride[4];
int64_t output_ne[4];
size_t output_stride[4];
size_t local_buffer_elems;
int64_t ir;
int64_t dr;
TPipe pipe;
GlobalTensor<float> input_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_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_f32(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR input_nb_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];
size_t input_nb_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(input_nb_gm, input_nb_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_F32 op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}

69
ggml/src/ggml-cann/kernels/get_row_f16.cpp → ggml/src/ggml-cann/kernels/get_row_float.cpp
View File

@ -5,14 +5,14 @@ using namespace AscendC;
#define BUFFER_NUM 2
class GET_ROW_F16 {
public:
__aicore__ inline GET_ROW_F16() {}
template<typename ROW_TYPE>
class GET_ROW_FLOAT {
public:
__aicore__ inline GET_ROW_FLOAT() {}
__aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
int64_t *input_ne_ub, size_t *input_nb_ub,
int64_t *indices_ne_ub, size_t *indices_nb_ub,
int64_t *output_ne_ub, size_t *output_nb_ub) {
// TODO, use template for F16/f32
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
@ -41,16 +41,18 @@ class GET_ROW_F16 {
ir = dr * op_block_idx + tails;
}
input_gm.SetGlobalBuffer((__gm__ half *)input);
input_gm.SetGlobalBuffer((__gm__ ROW_TYPE *)input);
indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
output_gm.SetGlobalBuffer((__gm__ float *)output);
uint64_t input_local_buffer_size = ((input_ne[0] * sizeof(half) + 31)
& ~31);
uint64_t output_local_buffer_size = ((input_ne[0] * sizeof(float) + 31)
& ~31);
local_buffer_elems = input_local_buffer_size / sizeof(half);
uint64_t input_local_buffer_size = ((input_ne[0] * sizeof(ROW_TYPE) + 31) & ~31);
uint64_t output_local_buffer_size = 0;
if constexpr (std::is_same<ROW_TYPE, half>::value) {
output_local_buffer_size = ((input_ne[0] * sizeof(float) + 31) & ~31);
} else if constexpr (std::is_same<ROW_TYPE, float>::value) {
output_local_buffer_size = input_local_buffer_size;
}
local_buffer_elems = input_local_buffer_size / sizeof(ROW_TYPE);
// TODO, consider long row that can't put in UB.
// All data should asign to 32. It's ok because all data is align to 32.
@ -59,15 +61,15 @@ class GET_ROW_F16 {
}
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
LocalTensor<half> input_local = input_queue.AllocTensor<half>();
LocalTensor<ROW_TYPE> input_local = input_queue.AllocTensor<ROW_TYPE>();
size_t tail = len % 32;
len = len & ~31;
DataCopy(input_local, input_gm[offset], len);
if(tail != 0) {
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(half);
DataCopyPadExtParams<half> padParams;
dataCopyParams.blockLen = tail * sizeof(ROW_TYPE);
DataCopyPadExtParams<ROW_TYPE> padParams;
DataCopyPad(input_local[len], input_gm[offset + len],
dataCopyParams, padParams);
}
@ -112,11 +114,16 @@ class GET_ROW_F16 {
indices_ne2_idx * output_stride[3];
copy_in(input_offset, input_ne[0]);
LocalTensor<half> input_local = input_queue.DeQue<half>();
LocalTensor<ROW_TYPE> input_local = input_queue.DeQue<ROW_TYPE>();
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
Cast(output_local, input_local, RoundMode::CAST_NONE,
local_buffer_elems);
if constexpr (std::is_same<ROW_TYPE, half>::value) {
Cast(output_local, input_local, RoundMode::CAST_NONE,
local_buffer_elems);
} else if constexpr (std::is_same<ROW_TYPE, float>::value) {
DataCopy(output_local, input_local, local_buffer_elems);
}
output_queue.EnQue(output_local);
copy_out(output_offset, input_ne[0]);
@ -145,7 +152,7 @@ class GET_ROW_F16 {
int64_t dr;
TPipe pipe;
GlobalTensor<half> input_gm;
GlobalTensor<ROW_TYPE> input_gm;
GlobalTensor<int32_t> indices_gm;
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
@ -179,7 +186,31 @@ extern "C" __global__ __aicore__ void ascendc_get_row_f16(
copy_to_ub(output_ne_gm, output_ne_ub, 32);
copy_to_ub(output_nb_gm, output_nb_ub, 32);
GET_ROW_F16 op;
GET_ROW_FLOAT<half> op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();
}
extern "C" __global__ __aicore__ void ascendc_get_row_f32(
GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
GM_ADDR input_ne_gm, GM_ADDR input_nb_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];
size_t input_nb_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(input_nb_gm, input_nb_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_FLOAT<float> op;
op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
op.calculate();