mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 13:28:50 +01:00
ggml : parallelize FP32 conversion when using BLAS (#5045)
* 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phase can be run in multithread * multithreaded dequantize in mul_mat when using blas library * minor fixes * update outdated comment * fix coding style * simplify code Co-authored-by: Georgi Gerganov 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
GitHub
parent
3ce7e8f8e7
commit
780e24a22e
178
ggml.c
178
ggml.c
@ -7810,6 +7810,9 @@ static void ggml_compute_forward_acc_f32(
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bool inplace = (bool) ((int32_t *) dst->op_params)[4];
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if (!inplace && (params->type == GGML_TASK_INIT)) {
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if (params->ith != 0) {
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return;
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}
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// memcpy needs to be synchronized across threads to avoid race conditions.
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// => do it in INIT phase
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memcpy(
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@ -9952,11 +9955,30 @@ static void ggml_compute_forward_mul_mat(
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
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if (ggml_compute_forward_mul_mat_use_blas(dst)) {
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if (params->ith != 0) {
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return;
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}
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const int64_t ne_plane = ne01*ne00;
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const int64_t desired_wsize = ne13*ne12*ne_plane*sizeof(float);
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UNUSED(desired_wsize);
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if (params->type == GGML_TASK_INIT) {
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if (type != GGML_TYPE_F32) {
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assert(params->wsize >= desired_wsize);
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// parallelize by src0 rows
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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// broadcast src0 into src1 across 2nd,3rd dimension
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const int64_t i03 = i13/r3;
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const int64_t i02 = i12/r2;
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const void * x = (char *) src0->data + i02*nb02 + i03*nb03;
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float * const wdata = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
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ggml_to_float_t const to_float = type_traits[type].to_float;
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for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
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to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00);
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}
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}
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}
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}
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return;
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}
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@ -9964,9 +9986,14 @@ static void ggml_compute_forward_mul_mat(
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return;
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}
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// perform sgemm, parallelization controlled by blas lib
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if (ith != 0) {
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return;
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}
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const int64_t tgemm0 = ggml_perf_time_us();
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for (int64_t i13 = 0; i13 < ne13; i13++) {
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for (int64_t i12 = 0; i12 < ne12; i12++) {
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// broadcast src0 into src1 across 2nd,3rd dimension
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const int64_t i03 = i13/r3;
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const int64_t i02 = i12/r2;
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@ -9975,17 +10002,7 @@ static void ggml_compute_forward_mul_mat(
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float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
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if (type != GGML_TYPE_F32) {
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float * const wdata = params->wdata;
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ggml_to_float_t const to_float = type_traits[type].to_float;
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size_t id = 0;
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for (int64_t i01 = 0; i01 < ne01; ++i01) {
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to_float((const char *) x + i01*nb01, wdata + id, ne00);
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id += ne00;
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}
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assert(id*sizeof(float) <= params->wsize);
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x = wdata;
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x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane;
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}
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cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
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@ -9995,6 +10012,7 @@ static void ggml_compute_forward_mul_mat(
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0.0f, d, ne01);
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}
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}
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//printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2);
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//printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
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@ -10003,6 +10021,9 @@ static void ggml_compute_forward_mul_mat(
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#endif
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if (params->type == GGML_TASK_INIT) {
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if (ith != 0) {
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return;
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}
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if (src1->type != vec_dot_type) {
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char * wdata = params->wdata;
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const size_t row_size = ggml_row_size(vec_dot_type, ne10);
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@ -10167,6 +10188,9 @@ static void ggml_compute_forward_mul_mat_id(
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#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
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if (params->type == GGML_TASK_INIT) {
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if (ith != 0) {
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return;
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}
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char * wdata = params->wdata;
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if (src1->type != vec_dot_type) {
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const size_t row_size = ggml_row_size(vec_dot_type, ne10);
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@ -10352,6 +10376,9 @@ static void ggml_compute_forward_out_prod_f32(
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return;
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}
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#endif
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if (ith != 0) {
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return;
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}
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ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
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return;
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}
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@ -10535,6 +10562,9 @@ static void ggml_compute_forward_out_prod_q_f32(
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// TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST)
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if (params->type == GGML_TASK_INIT) {
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if (ith != 0) {
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return;
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}
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ggml_vec_set_f32(ne0*ne1*ne2*ne3, dst->data, 0);
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return;
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}
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@ -10719,6 +10749,9 @@ static void ggml_compute_forward_set_f32(
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bool inplace = (bool) ((int32_t *) dst->op_params)[4];
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if (!inplace && (params->type == GGML_TASK_INIT)) {
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if (params->ith != 0) {
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return;
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}
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// memcpy needs to be synchronized across threads to avoid race conditions.
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// => do it in INIT phase
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memcpy(
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@ -11043,6 +11076,9 @@ static void ggml_compute_forward_get_rows_back_f32_f16(
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// ggml_compute_forward_dup_same_cont(params, opt0, dst);
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if (params->type == GGML_TASK_INIT) {
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if (params->ith != 0) {
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return;
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}
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memset(dst->data, 0, ggml_nbytes(dst));
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}
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@ -11077,6 +11113,9 @@ static void ggml_compute_forward_get_rows_back_f32(
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// ggml_compute_forward_dup_same_cont(params, opt0, dst);
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if (params->type == GGML_TASK_INIT) {
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if (params->ith != 0) {
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return;
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}
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memset(dst->data, 0, ggml_nbytes(dst));
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}
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@ -11214,6 +11253,9 @@ static void ggml_compute_forward_diag_mask_f32(
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GGML_ASSERT(n_past >= 0);
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if (!inplace && (params->type == GGML_TASK_INIT)) {
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if (ith != 0) {
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return;
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}
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// memcpy needs to be synchronized across threads to avoid race conditions.
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// => do it in INIT phase
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GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
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@ -12184,6 +12226,9 @@ static void ggml_compute_forward_conv_transpose_1d_f16_f32(
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GGML_ASSERT(nb10 == sizeof(float));
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if (params->type == GGML_TASK_INIT) {
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if (ith != 0) {
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return;
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}
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memset(params->wdata, 0, params->wsize);
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// permute kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
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@ -12278,6 +12323,9 @@ static void ggml_compute_forward_conv_transpose_1d_f32(
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GGML_ASSERT(nb10 == sizeof(float));
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if (params->type == GGML_TASK_INIT) {
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if (ith != 0) {
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return;
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}
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memset(params->wdata, 0, params->wsize);
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// prepare kernel data (src0) from (K x Cout x Cin) to (Cin x K x Cout)
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@ -12502,6 +12550,9 @@ static void ggml_compute_forward_conv_transpose_2d(
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GGML_ASSERT(nb10 == sizeof(float));
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if (params->type == GGML_TASK_INIT) {
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if (ith != 0) {
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return;
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}
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memset(params->wdata, 0, params->wsize);
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// permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
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@ -14116,6 +14167,9 @@ static void ggml_compute_forward_add_rel_pos_f32(
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const bool inplace = (bool) ((int32_t *) dst->op_params)[0];
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if (!inplace && params->type == GGML_TASK_INIT) {
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if (params->ith != 0) {
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return;
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}
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memcpy((char *) dst->data, (char *) src0->data, ggml_nbytes(dst));
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return;
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}
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@ -16411,6 +16465,7 @@ struct ggml_compute_state_shared {
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// synchronization primitives
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atomic_int n_active; // num active threads
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atomic_int node_n; // active graph node
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atomic_int node_task; // active graph node task phase
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bool (*abort_callback)(void * data); // abort ggml_graph_compute when true
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void * abort_callback_data;
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@ -16658,6 +16713,34 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
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return n_tasks;
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}
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static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_compute_state * state, const bool do_yield) {
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// wait for other threads to finish
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const int last_node_n = * node_n;
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while (true) {
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if (do_yield) {
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sched_yield();
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}
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* node_n = atomic_load(&state->shared->node_n);
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if (* node_n != last_node_n) break;
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}
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}
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static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) {
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// wait for other threads to finish
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const int last_task_phase = * task_phase;
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while (true) {
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if (do_yield) {
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sched_yield();
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}
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* task_phase = atomic_load(&state->shared->node_task);
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if (* task_phase != last_task_phase) break;
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}
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}
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static thread_ret_t ggml_graph_compute_thread(void * data) {
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struct ggml_compute_state * state = (struct ggml_compute_state *) data;
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@ -16669,6 +16752,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
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set_numa_thread_affinity(state->ith, n_threads);
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int node_n = -1;
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int task_phase = GGML_TASK_FINALIZE;
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while (true) {
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if (cplan->abort_callback && cplan->abort_callback(cplan->abort_callback_data)) {
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@ -16708,13 +16792,13 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
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params.nth = n_tasks;
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if (n_tasks == 1) {
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/* INIT */
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if (GGML_OP_HAS_INIT[node->op]) {
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params.type = GGML_TASK_INIT;
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ggml_compute_forward(¶ms, node);
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}
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if (n_tasks == 1) {
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// TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1,
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// they do something more efficient than spinning (?)
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params.type = GGML_TASK_COMPUTE;
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@ -16735,38 +16819,24 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
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}
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}
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task_phase = GGML_TASK_INIT;
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atomic_store(&state->shared->n_active, n_threads);
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atomic_store(&state->shared->node_n, node_n);
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atomic_store(&state->shared->node_task, task_phase);
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} else {
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// wait for other threads to finish
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const int last = node_n;
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const bool do_yield = last < 0 || cgraph->nodes[last]->op == GGML_OP_MUL_MAT;
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while (true) {
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// TODO: this sched_yield can have significant impact on the performance - either positive or negative
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// depending on the workload and the operating system.
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// since it is not clear what is the best approach, it should potentially become user-configurable
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// ref: https://github.com/ggerganov/ggml/issues/291
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// UPD: adding the do_yield flag seems to resolve the issue universally
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if (do_yield) {
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sched_yield();
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}
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node_n = atomic_load(&state->shared->node_n);
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if (node_n != last) break;
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};
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ggml_graph_compute_thread_sync_node(&node_n, state, false);
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ggml_graph_compute_thread_sync_task(&task_phase, state, false);
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}
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// check if we should stop
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if (node_n >= cgraph->n_nodes) break;
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/* COMPUTE */
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/* INIT & COMPUTE */
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struct ggml_tensor * node = cgraph->nodes[node_n];
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const int n_tasks = ggml_get_n_tasks(node, n_threads);
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struct ggml_compute_params params = {
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/*.type =*/ GGML_TASK_COMPUTE,
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/*.type =*/ GGML_TASK_INIT,
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/*.ith =*/ state->ith,
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/*.nth =*/ n_tasks,
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/*.wsize =*/ cplan->work_size,
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@ -16774,10 +16844,41 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
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};
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if (state->ith < n_tasks) {
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if (GGML_OP_HAS_INIT[node->op]) {
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ggml_compute_forward(¶ms, node);
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}
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}
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if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
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task_phase = GGML_TASK_COMPUTE;
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atomic_store(&state->shared->n_active, n_threads);
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atomic_store(&state->shared->node_task, task_phase);
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}
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else {
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// TODO: this sched_yield can have significant impact on the performance - either positive or negative
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// depending on the workload and the operating system.
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// since it is not clear what is the best approach, it should potentially become user-configurable
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// ref: https://github.com/ggerganov/ggml/issues/291
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// UPD: adding the do_yield flag seems to resolve the issue universally
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const bool do_yield = node_n < 0 || cgraph->nodes[node_n]->op == GGML_OP_MUL_MAT;
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ggml_graph_compute_thread_sync_task(&task_phase, state, do_yield);
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}
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if (state->ith < n_tasks) {
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params.type = GGML_TASK_COMPUTE;
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ggml_compute_forward(¶ms, node);
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}
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if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) {
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task_phase = GGML_TASK_FINALIZE;
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atomic_store(&state->shared->n_active, n_threads);
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atomic_store(&state->shared->node_task, task_phase);
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}
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else {
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ggml_graph_compute_thread_sync_task(&task_phase, state, false);
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}
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}
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return GGML_EXIT_SUCCESS;
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}
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@ -16832,8 +16933,8 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
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if (ggml_compute_forward_mul_mat_use_blas(node)) {
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if (node->src[0]->type != GGML_TYPE_F32) {
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// here we need memory just for single 2D matrix from src0
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cur = ggml_type_size(GGML_TYPE_F32)*(node->src[0]->ne[0]*node->src[0]->ne[1]);
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// here we need memory for fully dequantized matrix from src0
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cur = ggml_type_size(GGML_TYPE_F32)*ggml_nelements(node->src[0]);
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}
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} else
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#endif
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@ -16987,6 +17088,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) {
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/*.n_threads =*/ n_threads,
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/*.n_active =*/ n_threads,
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/*.node_n =*/ -1,
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/*.node_task =*/ GGML_TASK_FINALIZE,
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/*.abort_callback =*/ NULL,
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/*.abort_callback_data =*/ NULL,
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};
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