fix warning in ggml.c (#5)
Co-authored-by: syx <yixinsong@sjtu.edu.com>
This commit is contained in:
Jeremy Song
Holden
parent
1557b81743
commit
22ab495a79
1 changed files with 47 additions and 58 deletions
105
ggml.c
105
ggml.c
|
|
@ -13952,7 +13952,7 @@ static void ggml_compute_forward_mul_mat_sparse_head(
|
|||
|
||||
int64_t ir010 = dr0*ith0;
|
||||
// const int64_t ir011 = MIN(ir010 + dr0, nr0);
|
||||
const int64_t ir011 = ir010 + dr0;
|
||||
// const int64_t ir011 = ir010 + dr0;
|
||||
|
||||
const int64_t ir110 = dr1*ith1;
|
||||
const int64_t ir111 = MIN(ir110 + dr1, nr1);
|
||||
|
|
@ -13969,13 +13969,13 @@ static void ggml_compute_forward_mul_mat_sparse_head(
|
|||
assert(ne13 % ne03 == 0);
|
||||
|
||||
// block-tiling attempt
|
||||
const int64_t blck_0 = 16;
|
||||
// const int64_t blck_0 = 16;
|
||||
const int64_t blck_1 = 16;
|
||||
|
||||
// attempt to reduce false-sharing (does not seem to make a difference)
|
||||
float tmp[16];
|
||||
// float tmp[16];
|
||||
float *ffdata = (float *)dst->src[2]->data;
|
||||
int *gid = (int *)dst->src[3]->data;
|
||||
// int *gid = (int *)dst->src[3]->data;
|
||||
while(true) {
|
||||
ir010 = atomic_fetch_add(params->aic, dr0);
|
||||
for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
|
||||
|
|
@ -14210,12 +14210,12 @@ static void ggml_compute_forward_mul_mat_sparse(
|
|||
assert(ne13 % ne03 == 0);
|
||||
|
||||
// block-tiling attempt
|
||||
const int64_t blck_0 = 16;
|
||||
// const int64_t blck_0 = 16;
|
||||
const int64_t blck_1 = 16;
|
||||
int total = 0;
|
||||
// int total = 0;
|
||||
|
||||
// attempt to reduce false-sharing (does not seem to make a difference)
|
||||
float tmp[16];
|
||||
// float tmp[16];
|
||||
float *ffdata = (float *)dst->src[2]->data;
|
||||
int *gid = (int *)dst->src[3]->data;
|
||||
float *predictor_data = (float *)dst->src[2]->data;
|
||||
|
|
@ -14291,13 +14291,14 @@ static void ggml_compute_forward_mul_mat_sparse(
|
|||
}
|
||||
|
||||
// vz = alpha * vx + vy
|
||||
static void ggml_axpy_normal_f16(const int n, const ggml_fp16_t * vx, const ggml_fp16_t * restrict vy, const void* restrict vz, ggml_fp16_t alpha) {
|
||||
static void ggml_axpy_normal_f16(const int n, const ggml_fp16_t * vx, const ggml_fp16_t * restrict vy, void* restrict vz, ggml_fp16_t alpha) {
|
||||
float *res = (float *)vz;
|
||||
for (int i = 0; i < n; i++) {
|
||||
res[i] = res[i] + (GGML_FP16_TO_FP32(vx[i])*GGML_FP16_TO_FP32(alpha));
|
||||
}
|
||||
(void) vy;
|
||||
}
|
||||
static void ggml_axpy_avx_f16(const int n, const ggml_fp16_t * restrict vx, const ggml_fp16_t * restrict vy, void* restrict vz, ggml_fp16_t alpha) {
|
||||
static void ggml_axpy_avx_f16(const int n, const ggml_fp16_t * restrict vx, const ggml_fp16_t * vy, void* vz, ggml_fp16_t alpha) {
|
||||
#if defined(__AVX2__)
|
||||
float *result = (float *)vz;
|
||||
float alpha_f32 = GGML_FP16_TO_FP32(alpha);
|
||||
|
|
@ -14316,7 +14317,7 @@ static void ggml_axpy_avx_f16(const int n, const ggml_fp16_t * restrict vx, cons
|
|||
res[i] = res[i] + (GGML_FP16_TO_FP32(vx[i])*alpha_convert);
|
||||
}
|
||||
#endif
|
||||
|
||||
(void)vy;
|
||||
}
|
||||
atomic_flag g_axpy_dense_lock = ATOMIC_FLAG_INIT;
|
||||
static void ggml_compute_forward_mul_mat_axpy_dense(
|
||||
|
|
@ -14329,14 +14330,14 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
|
|||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS;
|
||||
|
||||
const int ith = params->ith;
|
||||
// const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
|
||||
const bool src1_cont = ggml_is_contiguous(src1);
|
||||
// const bool src1_cont = ggml_is_contiguous(src1);
|
||||
|
||||
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
// ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
|
||||
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
|
||||
|
||||
|
|
@ -14356,8 +14357,8 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
|
|||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// broadcast factors
|
||||
const int64_t r2 = ne12/ne02;
|
||||
const int64_t r3 = ne13/ne03;
|
||||
// const int64_t r2 = ne12/ne02;
|
||||
// const int64_t r3 = ne13/ne03;
|
||||
|
||||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
|
@ -14387,7 +14388,7 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
|
|||
}
|
||||
|
||||
ggml_fp16_t* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
|
||||
const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
|
||||
// const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
|
||||
|
||||
struct ggml_tensor *src2 = dst->src[2];
|
||||
|
||||
|
|
@ -14399,7 +14400,7 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
|
|||
// const int64_t ir11 = MIN(ir10 + dr, src2->ne[0]);
|
||||
|
||||
// src1 rows
|
||||
const int64_t nr1 = ne11*ne12*ne13;
|
||||
// const int64_t nr1 = ne11*ne12*ne13;
|
||||
// float *idx = src2->data;
|
||||
// int *gid = (int *)(dst->src[3]->data);
|
||||
// printf("down %d up %d ne00 %d\n", ir10, ir11, ne00);
|
||||
|
|
@ -14407,7 +14408,7 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
|
|||
float vec[ne00*4];
|
||||
void *vy = vec;
|
||||
memset(vy, 0, ne00*4);
|
||||
char* src0_row = (const char *) src0->data;
|
||||
char* src0_row = (char *) src0->data;
|
||||
while(true) {
|
||||
const int ir0 = atomic_fetch_add(params->aic, dr);
|
||||
for (int64_t ir1 = ir0; ir1 < ir0+dr; ir1++) {
|
||||
|
|
@ -14417,7 +14418,7 @@ static void ggml_compute_forward_mul_mat_axpy_dense(
|
|||
// if (idx[ir1] < 0.0f)
|
||||
// continue;
|
||||
// ggml_axpy_normal_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
|
||||
ggml_axpy_avx_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
|
||||
ggml_axpy_avx_f16(ne00, (ggml_fp16_t *)(src0_row+nb01*ir1), (ggml_fp16_t *)vy, vy, wdata[ir1]);
|
||||
}
|
||||
if (ir0 + dr >= nr)
|
||||
break;
|
||||
|
|
@ -14475,9 +14476,9 @@ static void ggml_compute_forward_mul_mat_axpy(
|
|||
|
||||
const enum ggml_type type = src0->type;
|
||||
|
||||
const bool src1_cont = ggml_is_contiguous(src1);
|
||||
// const bool src1_cont = ggml_is_contiguous(src1);
|
||||
|
||||
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
// ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
|
||||
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
|
||||
|
||||
|
|
@ -14497,8 +14498,8 @@ static void ggml_compute_forward_mul_mat_axpy(
|
|||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// broadcast factors
|
||||
const int64_t r2 = ne12/ne02;
|
||||
const int64_t r3 = ne13/ne03;
|
||||
// const int64_t r2 = ne12/ne02;
|
||||
// const int64_t r3 = ne13/ne03;
|
||||
|
||||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
|
@ -14550,7 +14551,7 @@ static void ggml_compute_forward_mul_mat_axpy(
|
|||
|
||||
float vec[ne00*4];
|
||||
void *vy = vec;
|
||||
char* src0_row = (const char *) src0->data;
|
||||
char* src0_row = (char *) src0->data;
|
||||
ggml_fp16_t * src1_ptr = NULL;
|
||||
for (int col_idx = 0; col_idx < nr1; col_idx++) {
|
||||
src1_ptr = (ggml_fp16_t *)((char *)wdata + col_idx * row_size);
|
||||
|
|
@ -14571,7 +14572,7 @@ static void ggml_compute_forward_mul_mat_axpy(
|
|||
if (idx[ir1] < -0.0f)
|
||||
continue;
|
||||
// ggml_axpy_normal_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
|
||||
ggml_axpy_avx_f16(ne00, src0_row+nb01*ir1, vy, vy, src1_ptr[ir1]);
|
||||
ggml_axpy_avx_f16(ne00, (ggml_fp16_t *)(src0_row+nb01*ir1), (ggml_fp16_t *)vy, vy, src1_ptr[ir1]);
|
||||
}
|
||||
|
||||
// 获取锁
|
||||
|
|
@ -14625,9 +14626,9 @@ static void ggml_compute_forward_mul_mat_axpy_q4_0(
|
|||
|
||||
const enum ggml_type type = src0->type;
|
||||
|
||||
const bool src1_cont = ggml_is_contiguous(src1);
|
||||
// const bool src1_cont = ggml_is_contiguous(src1);
|
||||
|
||||
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
// ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
|
||||
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
|
||||
|
||||
|
|
@ -14647,8 +14648,8 @@ static void ggml_compute_forward_mul_mat_axpy_q4_0(
|
|||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// broadcast factors
|
||||
const int64_t r2 = ne12/ne02;
|
||||
const int64_t r3 = ne13/ne03;
|
||||
// const int64_t r2 = ne12/ne02;
|
||||
// const int64_t r3 = ne13/ne03;
|
||||
|
||||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
|
@ -14698,10 +14699,10 @@ static void ggml_compute_forward_mul_mat_axpy_q4_0(
|
|||
|
||||
float vec[ne00*4];
|
||||
void *vy = vec;
|
||||
char* src0_row = (const char *) src0->data;
|
||||
char* src0_row = (char *) src0->data;
|
||||
for (int col_idx = 0; col_idx < nr1; col_idx++) {
|
||||
// const block_q8_0 * restrict nerual = wdata;
|
||||
const block_q8_0 *restrict nerual = ((char *)wdata + col_idx * row_size);
|
||||
const block_q8_0 *restrict nerual = (block_q8_0 *)((char *)wdata + col_idx * row_size);
|
||||
idx = (float *)((char *)src2->data + col_idx * idx_row_size);
|
||||
memset(vy, 0, ne00 * 4);
|
||||
// while(true) {
|
||||
|
|
@ -14774,14 +14775,14 @@ static void ggml_compute_forward_mul_mat_axpy_head(
|
|||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS;
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
// const int ith = params->ith;
|
||||
// const int nth = params->nth;
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
|
||||
const bool src1_cont = ggml_is_contiguous(src1);
|
||||
// const bool src1_cont = ggml_is_contiguous(src1);
|
||||
|
||||
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
// ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
|
||||
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
|
||||
|
||||
|
|
@ -14801,8 +14802,8 @@ static void ggml_compute_forward_mul_mat_axpy_head(
|
|||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// broadcast factors
|
||||
const int64_t r2 = ne12/ne02;
|
||||
const int64_t r3 = ne13/ne03;
|
||||
// const int64_t r2 = ne12/ne02;
|
||||
// const int64_t r3 = ne13/ne03;
|
||||
|
||||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
|
@ -14832,7 +14833,7 @@ static void ggml_compute_forward_mul_mat_axpy_head(
|
|||
}
|
||||
|
||||
const ggml_fp16_t* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
|
||||
const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
|
||||
// const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
|
||||
|
||||
struct ggml_tensor *src2 = dst->src[2];
|
||||
int chunk = ne00 / 32;
|
||||
|
|
@ -14845,15 +14846,15 @@ static void ggml_compute_forward_mul_mat_axpy_head(
|
|||
// const int64_t ir11 = MIN(ir10 + dr, src2->ne[0]);
|
||||
|
||||
// src1 rows
|
||||
const int64_t nr1 = ne11*ne12*ne13;
|
||||
float *idx = src2->data;
|
||||
int *gid = (int *)(dst->src[3]->data);
|
||||
// const int64_t nr1 = ne11*ne12*ne13;
|
||||
// float *idx = src2->data;
|
||||
// int *gid = (int *)(dst->src[3]->data);
|
||||
// printf("down %d up %d ne00 %d\n", ir10, ir11, ne00);
|
||||
|
||||
float vec[ne00*4];
|
||||
void *vy = vec;
|
||||
memset(vy, 0, ne00*4);
|
||||
char* src0_row = (const char *) src0->data;
|
||||
char* src0_row = (char *) src0->data;
|
||||
while (true) {
|
||||
const int ir0 = atomic_fetch_add(params->aic, dr);
|
||||
// int id = ir0 >> 7;
|
||||
|
|
@ -14862,7 +14863,7 @@ static void ggml_compute_forward_mul_mat_axpy_head(
|
|||
for (int64_t ir1 = ir0; ir1 < ir0+dr; ir1++) {
|
||||
if (ir1 >= nr) break;
|
||||
// ggml_axpy_normal_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
|
||||
ggml_axpy_avx_f16(ne00, src0_row+nb01*ir1, vy, vy, wdata[ir1]);
|
||||
ggml_axpy_avx_f16(ne00, (ggml_fp16_t *)(src0_row+nb01*ir1), (ggml_fp16_t *)vy, vy, wdata[ir1]);
|
||||
}
|
||||
if (ir0 + dr >= nr)
|
||||
break;
|
||||
|
|
@ -15746,6 +15747,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
GGML_ASSERT(false); // TODO: not implemented
|
||||
} break;
|
||||
case GGML_OP_MUL_MAT:
|
||||
case GGML_OP_AXPY:
|
||||
{
|
||||
// https://cs231n.github.io/optimization-2/#staged
|
||||
// # forward pass
|
||||
|
|
@ -16737,20 +16739,7 @@ static void ggml_graph_compute_perf_stats_node_gpu(struct ggml_tensor * node, co
|
|||
node->perf_cycles += cycles_cur;
|
||||
node->perf_time_us += time_us_cur;
|
||||
}
|
||||
void busy_wait_cycles(int cycles) {
|
||||
struct timespec ts_start, ts_end;
|
||||
|
||||
clock_gettime(CLOCK_MONOTONIC, &ts_start);
|
||||
|
||||
while (1) {
|
||||
clock_gettime(CLOCK_MONOTONIC, &ts_end);
|
||||
long diff_ns = (ts_end.tv_sec - ts_start.tv_sec) * 1000000000 +
|
||||
(ts_end.tv_nsec - ts_start.tv_nsec);
|
||||
if (diff_ns >= cycles) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
|
||||
int n_tasks = 0;
|
||||
|
|
@ -17164,8 +17153,8 @@ static thread_ret_t ggml_graph_compute_thread_hybrid(void * data) {
|
|||
/*.type =*/GGML_TASK_COMPUTE,
|
||||
/*.ith =*/0,
|
||||
/*.nth =*/1,
|
||||
/*.wsize =*/NULL,
|
||||
/*.wdata =*/NULL,
|
||||
/*.wsize =*/0,
|
||||
/*.wdata =*/0,
|
||||
/*.aic =*/0,
|
||||
};
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue