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CANN: Support Ascend310P to accelerate F32 and F16 Model (#10216)

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* CANN Support Ascend310P to accelerate F32 and F16 Model

* Add compile option soc type macro ASCEND_310P to ggml-cann lib

* Remove unused code

* Remove the ascend soc_type hard code compile option in CMakelist.txt
This commit is contained in:
leo-pony 2024-11-22 14:07:20 +08:00 • committed by GitHub
parent a5e47592b6
commit c18610b4ee
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7 changed files with 123 additions and 41 deletions
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7
ggml/src/ggml-cann/kernels/CMakeLists.txt
View file

@ -1,7 +1,3 @@
if (NOT SOC_TYPE)
set (SOC_TYPE "Ascend910B3")
endif()
file(GLOB SRC_FILES
get_row_f32.cpp
get_row_f16.cpp
@ -13,7 +9,6 @@ file(GLOB SRC_FILES
dup.cpp
)
string(TOLOWER ${SOC_TYPE} SOC_VERSION)
set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR})
set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim")
@ -30,4 +25,6 @@ ascendc_library(ascendc_kernels STATIC
${SRC_FILES}
)
message(STATUS "CANN: compile ascend kernels witch SOC_VERSION:${SOC_VERSION}.")
ascendc_compile_definitions(ascendc_kernels PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
# ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)

32
ggml/src/ggml-cann/kernels/dup.cpp
View file

@ -5,6 +5,7 @@
using namespace AscendC;
#define BUFFER_NUM 2
const int64_t SUPPORTED_MAX_DIM = 65535; // currently the limit of max block dim supportted by dup kernel is 65535template <typename SRC_T, typename DST_T>
template <typename SRC_T, typename DST_T>
class DupByRows {
@ -19,6 +20,7 @@ class DupByRows {
// Input has four dims.
int64_t op_block_num = GetBlockNum();
int64_t op_block_idx = GetBlockIdx();
assert(op_block_idx < SUPPORTED_MAX_DIM && op_block_idx >= 0, "Invalid block index:%d, max is:%d\n", op_block_idx, SUPPORTED_MAX_DIM);
// param
num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3];
@ -51,24 +53,36 @@ class DupByRows {
__aicore__ inline void copy_in() {
LocalTensor<SRC_T> src_local = src_queue.AllocTensor<SRC_T>();
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = num_elem * sizeof(SRC_T);
DataCopyPadExtParams<SRC_T> padParams;
DataCopyPad(src_local, src_gm, dataCopyParams, padParams);
const size_t elem_per_block = 32 / sizeof(SRC_T);
size_t tail = num_elem % elem_per_block;
size_t cpy_elements_len = tail > 0 ? num_elem + 1 : num_elem;
DataCopy(src_local, src_gm, cpy_elements_len);
src_queue.EnQue(src_local);
}
__aicore__ inline void copy_out() {
LocalTensor<DST_T> dst_local = dst_queue.DeQue<DST_T>();
#ifdef ASCEND_310P
const size_t elem_per_block = 32 / sizeof(DST_T);
size_t tail = num_elem % elem_per_block;
size_t len = num_elem & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(dst_gm, dst_local, len);
}
if(tail != 0) {
for (size_t i = tail; i < elem_per_block; i++) {
dst_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(dst_gm[len], dst_local[len], elem_per_block);
SetAtomicNone();
}
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = num_elem * sizeof(DST_T);
DataCopyPad(dst_gm, dst_local, dataCopyParams);
#endif
dst_queue.FreeTensor(dst_local);
}

37
ggml/src/ggml-cann/kernels/get_row_f16.cpp
View file

@ -14,7 +14,7 @@ class GET_ROW_F16 {
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();
op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
@ -59,32 +59,42 @@ class GET_ROW_F16 {
}
__aicore__ inline void copy_in(uint32_t offset, size_t len) {
size_t origin_len = len;
LocalTensor<half> input_local = input_queue.AllocTensor<half>();
size_t tail = len % 32;
len = len & ~31;
DataCopy(input_local, input_gm[offset], len);
const size_t elem_per_block = 32 / sizeof(half);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if(tail != 0) {
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(half);
DataCopyPadExtParams<half> padParams;
DataCopyPad(input_local[len], input_gm[offset + len],
dataCopyParams, padParams);
len += elem_per_block;
}
DataCopy(input_local, input_gm[offset], len);
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);
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(output_gm[offset], output_local, len);
}
if(tail != 0) {
#ifdef ASCEND_310P
for (size_t i = tail; i < elem_per_block; i++) {
output_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(output_gm[offset + len], output_local[len], elem_per_block);
SetAtomicNone();
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
#endif
}
output_queue.FreeTensor(output_local);
}
@ -150,6 +160,7 @@ class GET_ROW_F16 {
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
int64_t op_block_idx;
};
template <typename T>

36
ggml/src/ggml-cann/kernels/get_row_f32.cpp
View file

@ -13,7 +13,7 @@ class GET_ROW_F32 {
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();
op_block_idx = GetBlockIdx();
for (int i = 0; i < 4; i++) {
input_ne[i] = input_ne_ub[i];
@ -55,31 +55,40 @@ class GET_ROW_F32 {
__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);
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
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);
len += elem_per_block;
}
DataCopy(input_local, input_gm[offset], len);
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);
const size_t elem_per_block = 32 / sizeof(float);
size_t tail = len % elem_per_block;
len = len & ~(elem_per_block - 1);
if (len > 0) {
DataCopy(output_gm[offset], output_local, len);
}
if(tail != 0) {
#ifdef ASCEND_310P
for (size_t i = tail; i < elem_per_block; i++) {
output_local[len + i].SetValue(0, 0);
}
SetAtomicAdd<float>();
DataCopy(output_gm[offset + len], output_local[len], elem_per_block);
SetAtomicNone();
#else
DataCopyExtParams dataCopyParams;
dataCopyParams.blockCount = 1;
dataCopyParams.blockLen = tail * sizeof(float);
DataCopyPad(output_gm[offset + len], output_local[len],
dataCopyParams);
#endif
}
output_queue.FreeTensor(output_local);
}
@ -144,6 +153,7 @@ class GET_ROW_F32 {
GlobalTensor<float> output_gm;
TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
int64_t op_block_idx;
};
template <typename T>

5
ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
View file

@ -110,9 +110,12 @@ class GET_ROW_Q4_0 {
LocalTensor<float> output_local = output_queue.AllocTensor<float>();
// TODO: cast more data to speed up.
#ifdef ASCEND_310P
// TODO: 310P support quantification
#else
Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0);
Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0);
#endif
// Only mul need compile by group.
half scale = scale_gm.GetValue(scale_offset);