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ggml : parallelize FP32 conversion when using BLAS (#5045)

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* make GGML_TASK_INIT phase can be run in multithread

* multithreaded dequantize in mul_mat when using blas library

* minor fixes

* update outdated comment
* fix coding style

* simplify code

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Reinforce-II
2024-01-22 21:15:08 +08:00
committed by GitHub
parent 3ce7e8f8e7
commit 780e24a22e
1 changed files with 150 additions and 48 deletions
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178
ggml.c
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@@ -7810,6 +7810,9 @@ static void ggml_compute_forward_acc_f32(
bool inplace = (bool) ((int32_t *) dst->op_params)[4];
if (!inplace && (params->type == GGML_TASK_INIT)) {
if (params->ith != 0) {
return;
}
// memcpy needs to be synchronized across threads to avoid race conditions.
// => do it in INIT phase
memcpy(
@@ -9952,11 +9955,30 @@ static void ggml_compute_forward_mul_mat(
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(dst)) {
if (params->ith != 0) {
return;
}
const int64_t ne_plane = ne01*ne00;
const int64_t desired_wsize = ne13*ne12*ne_plane*sizeof(float);
UNUSED(desired_wsize);
if (params->type == GGML_TASK_INIT) {
if (type != GGML_TYPE_F32) {
assert(params->wsize >= desired_wsize);
// parallelize by src0 rows
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
// broadcast src0 into src1 across 2nd,3rd dimension
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
float * const wdata = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
ggml_to_float_t const to_float = type_traits[type].to_float;
for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00);
}
}
}
}
return;
}
@@ -9964,9 +9986,14 @@ static void ggml_compute_forward_mul_mat(
return;
}
// perform sgemm, parallelization controlled by blas lib
if (ith != 0) {
return;
}
const int64_t tgemm0 = ggml_perf_time_us();
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
// broadcast src0 into src1 across 2nd,3rd dimension
const int64_t i03 = i13/r3;
const int64_t i02 = i12/r2;
@@ -9975,17 +10002,7 @@ static void ggml_compute_forward_mul_mat(
float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
if (type != GGML_TYPE_F32) {
float * const wdata = params->wdata;
ggml_to_float_t const to_float = type_traits[type].to_float;
size_t id = 0;
for (int64_t i01 = 0; i01 < ne01; ++i01) {
to_float((const char *) x + i01*nb01, wdata + id, ne00);
id += ne00;
}
assert(id*sizeof(float) <= params->wsize);
x = wdata;
x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
}
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
@@ -9995,6 +10012,7 @@ static void ggml_compute_forward_mul_mat(
0.0f, d, ne01);
}
}
//printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2);
//printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
@@ -10003,6 +10021,9 @@ static void ggml_compute_forward_mul_mat(
#endif
if (params->type == GGML_TASK_INIT) {
if (ith != 0) {
return;
}
if (src1->type != vec_dot_type) {
char * wdata = params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -10167,6 +10188,9 @@ static void ggml_compute_forward_mul_mat_id(
#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
if (params->type == GGML_TASK_INIT) {
if (ith != 0) {
return;
}
char * wdata = params->wdata;
if (src1->type != vec_dot_type) {
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
@@ -10352,6 +10376,9 @@ static void ggml_compute_forward_out_prod_f32(
return;
}
#endif
if (ith != 0) {
return;
}
ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
return;
}
@@ -10535,6 +10562,9 @@ static void ggml_compute_forward_out_prod_q_f32(
// TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
if (params->type == GGML_TASK_INIT) {
if (ith != 0) {
return;
}
ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
return;
}
@@ -10719,6 +10749,9 @@ static void ggml_compute_forward_set_f32(
bool inplace = (bool) ((int32_t *) dst->op_params)[4];
if (!inplace && (params->type == GGML_TASK_INIT)) {
if (params->ith != 0) {
return;
}
// memcpy needs to be synchronized across threads to avoid race conditions.
// => do it in INIT phase
memcpy(
@@ -11043,6 +11076,9 @@ static void ggml_compute_forward_get_rows_back_f32_f16(
// ggml_compute_forward_dup_same_cont(params, opt0, dst);
if (params->type == GGML_TASK_INIT) {
if (params->ith != 0) {
return;
}
memset(dst->data, 0, ggml_nbytes(dst));
}
@@ -11077,6 +11113,9 @@ static void ggml_compute_forward_get_rows_back_f32(
// ggml_compute_forward_dup_same_cont(params, opt0, dst);
if (params->type == GGML_TASK_INIT) {
if (params->ith != 0) {
return;
}
memset(dst->data, 0, ggml_nbytes(dst));
}
@@ -11214,6 +11253,9 @@ static void ggml_compute_forward_diag_mask_f32(
GGML_ASSERT(n_past >= 0);
if (!inplace && (params->type == GGML_TASK_INIT)) {
if (ith != 0) {
return;
}
// memcpy needs to be synchronized across threads to avoid race conditions.
// => do it in INIT phase
GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
@@ -12184,6 +12226,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
GGML_ASSERT(nb10 == sizeof(float));
if (params->type == GGML_TASK_INIT) {
if (ith != 0) {
return;
}
memset(params->wdata, 0, params->wsize);
// permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -12278,6 +12323,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
GGML_ASSERT(nb10 == sizeof(float));
if (params->type == GGML_TASK_INIT) {
if (ith != 0) {
return;
}
memset(params->wdata, 0, params->wsize);
// prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
@@ -12502,6 +12550,9 @@ static void ggml_compute_forward_conv_transpose_2d(
GGML_ASSERT(nb10 == sizeof(float));
if (params->type == GGML_TASK_INIT) {
if (ith != 0) {
return;
}
memset(params->wdata, 0, params->wsize);
// permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
@@ -14116,6 +14167,9 @@ static void ggml_compute_forward_add_rel_pos_f32(
const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
if (!inplace && params->type == GGML_TASK_INIT) {
if (params->ith != 0) {
return;
}
memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
return;
}
@@ -16411,6 +16465,7 @@ struct ggml_compute_state_shared {
// synchronization primitives
atomic_int n_active; // num active threads
atomic_int node_n; // active graph node
atomic_int node_task; // active graph node task phase
bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
void * abort_callback_data;
@@ -16658,6 +16713,34 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
return n_tasks;
}
static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) {
// wait for other threads to finish
const int last_node_n = * node_n;
while (true) {
if (do_yield) {
sched_yield();
}
* node_n = atomic_load(&state->shared->node_n);
if (* node_n != last_node_n) break;
}
}
static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) {
// wait for other threads to finish
const int last_task_phase = * task_phase;
while (true) {
if (do_yield) {
sched_yield();
}
* task_phase = atomic_load(&state->shared->node_task);
if (* task_phase != last_task_phase) break;
}
}
static thread_ret_t ggml_graph_compute_thread(void * data) {
struct ggml_compute_state * state = (struct ggml_compute_state *) data;
@@ -16669,6 +16752,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
set_numa_thread_affinity(state->ith, n_threads);
int node_n = -1;
int task_phase = GGML_TASK_FINALIZE;
while (true) {
if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
@@ -16708,13 +16792,13 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
params.nth = n_tasks;
if (n_tasks == 1) {
/* INIT */
if (GGML_OP_HAS_INIT[node->op]) {
params.type = GGML_TASK_INIT;
ggml_compute_forward(&params, node);
}
if (n_tasks == 1) {
// TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
// they do something more efficient than spinning (?)
params.type = GGML_TASK_COMPUTE;
@@ -16735,38 +16819,24 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
}
}
task_phase = GGML_TASK_INIT;
atomic_store(&state->shared->n_active, n_threads);
atomic_store(&state->shared->node_n, node_n);
atomic_store(&state->shared->node_task, task_phase);
} else {
// wait for other threads to finish
const int last = node_n;
const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
while (true) {
// TODO: this sched_yield can have significant impact on the performance - either positive or negative
// depending on the workload and the operating system.
// since it is not clear what is the best approach, it should potentially become user-configurable
// ref: https://github.com/ggerganov/ggml/issues/291
// UPD: adding the do_yield flag seems to resolve the issue universally
if (do_yield) {
sched_yield();
}
node_n = atomic_load(&state->shared->node_n);
if (node_n != last) break;
};
ggml_graph_compute_thread_sync_node(&node_n, state, false);
ggml_graph_compute_thread_sync_task(&task_phase, state, false);
}
// check if we should stop
if (node_n >= cgraph->n_nodes) break;
/* COMPUTE */
/* INIT & COMPUTE */
struct ggml_tensor * node = cgraph->nodes[node_n];
const int n_tasks = ggml_get_n_tasks(node, n_threads);
struct ggml_compute_params params = {
/*.type =*/ GGML_TASK_COMPUTE,
/*.type =*/ GGML_TASK_INIT,
/*.ith =*/ state->ith,
/*.nth =*/ n_tasks,
/*.wsize =*/ cplan->work_size,
@@ -16774,10 +16844,41 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
};
if (state->ith < n_tasks) {
if (GGML_OP_HAS_INIT[node->op]) {
ggml_compute_forward(&params, node);
}
}
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
task_phase = GGML_TASK_COMPUTE;
atomic_store(&state->shared->n_active, n_threads);
atomic_store(&state->shared->node_task, task_phase);
}
else {
// TODO: this sched_yield can have significant impact on the performance - either positive or negative
// depending on the workload and the operating system.
// since it is not clear what is the best approach, it should potentially become user-configurable
// ref: https://github.com/ggerganov/ggml/issues/291
// UPD: adding the do_yield flag seems to resolve the issue universally
const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT;
ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield);
}
if (state->ith < n_tasks) {
params.type = GGML_TASK_COMPUTE;
ggml_compute_forward(&params, node);
}
if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
task_phase = GGML_TASK_FINALIZE;
atomic_store(&state->shared->n_active, n_threads);
atomic_store(&state->shared->node_task, task_phase);
}
else {
ggml_graph_compute_thread_sync_task(&task_phase, state, false);
}
}
return GGML_EXIT_SUCCESS;
}
@@ -16832,8 +16933,8 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(node)) {
if (node->src[0]->type != GGML_TYPE_F32) {
// here we need memory just for single 2D matrix from src0
cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
// here we need memory for fully dequantized matrix from src0
cur = ggml_type_size(GGML_TYPE_F32)*ggml_nelements(node->src[0]);
}
} else
#endif
@@ -16987,6 +17088,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
/*.n_threads =*/ n_threads,
/*.n_active =*/ n_threads,
/*.node_n =*/ -1,
/*.node_task =*/ GGML_TASK_FINALIZE,
/*.abort_callback =*/ NULL,
/*.abort_callback_data =*/ NULL,
};