haraldwolff/llama.cpp
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ggml : add ggml_row_size() (fixes llama out of space) (#4461)

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* Fixes "Not enough space in the context's memory pool" encountered on certain models, which seems to be caused by some imprecision related to the automatic casting of floating point values

* do not cast to size_t, instead just use doubles

* ggml : add ggml_row_size(), deprecate ggml_type_sizef()

* ggml : fix row size compute to avoid overflows

* tests : fix sizey -> sizez

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
LostRuins 2023-12-14 20:13:33 +08:00 committed by GitHub
parent 55e87c3749
commit 20a68a7030
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4 changed files with 27 additions and 18 deletions
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14
examples/benchmark/benchmark-matmult.cpp
View file

@ -129,13 +129,13 @@ int main(int argc, char ** argv) {
const ggml_type qtype = GGML_TYPE_Q4_1;
size_t ctx_size = 0;
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizez*ggml_type_sizef(GGML_TYPE_F32);
ctx_size += sizex*sizey*ggml_type_sizef(qtype);
ctx_size += sizex*sizey*ggml_type_sizef(qtype);
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey);
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizez);
ctx_size += ggml_row_size(qtype, sizex*sizey);
ctx_size += ggml_row_size(qtype, sizex*sizey);
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
ctx_size += ggml_row_size(GGML_TYPE_F32, sizex*sizey); // BLAS
ctx_size += 1024*1024*16;
printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));

9
ggml.c
View file

@ -2011,8 +2011,13 @@ size_t ggml_type_size(enum ggml_type type) {
return type_traits[type].type_size;
}
float ggml_type_sizef(enum ggml_type type) {
return ((float)(type_traits[type].type_size))/type_traits[type].blck_size;
size_t ggml_row_size(enum ggml_type type, int64_t ne) {
assert(ne % ggml_blck_size(type) == 0);
return ggml_type_size(type)*ne/ggml_blck_size(type);
}
double ggml_type_sizef(enum ggml_type type) {
return ((double)(type_traits[type].type_size))/type_traits[type].blck_size;
}
const char * ggml_type_name(enum ggml_type type) {

10
ggml.h
View file

@ -641,9 +641,13 @@ extern "C" {
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
GGML_API int ggml_blck_size (enum ggml_type type);
GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
GGML_API int ggml_blck_size(enum ggml_type type);
GGML_API size_t ggml_type_size(enum ggml_type type); // size in bytes for all elements in a block
GGML_API size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
GGML_DEPRECATED(
GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
"use ggml_row_size() instead");
GGML_API const char * ggml_type_name(enum ggml_type type);
GGML_API const char * ggml_op_name (enum ggml_op op);

12
llama.cpp
View file

@ -1555,7 +1555,7 @@ static bool llama_kv_cache_init(
cache.cells.clear();
cache.cells.resize(n_ctx);
cache.buf.resize(n_elements*(ggml_type_sizef(ktype) + ggml_type_sizef(vtype)) + 2u*n_layer*ggml_tensor_overhead());
cache.buf.resize(ggml_row_size(ktype, n_elements) + ggml_row_size(vtype, n_elements) + 2u*n_layer*ggml_tensor_overhead());
memset(cache.buf.data, 0, cache.buf.size);
struct ggml_init_params params;
@ -3822,8 +3822,8 @@ static void llm_build_k_shift(
ggml_rope_custom_inplace(ctx,
ggml_view_3d(ctx, kv.k_l[il],
n_embd_head, n_head_kv, n_ctx,
ggml_type_sizef(kv.k_l[il]->type)*n_embd_head,
ggml_type_sizef(kv.k_l[il]->type)*n_embd_gqa,
ggml_row_size(kv.k_l[il]->type, n_embd_head),
ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
0),
K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
@ -3852,7 +3852,7 @@ static void llm_build_kv_store(
cb(v_cur_t, "v_cur_t", il);
struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_gqa,
(ggml_type_sizef(kv.k_l[il]->type)*n_embd_gqa)*kv_head);
(ggml_row_size(kv.k_l[il]->type, n_embd_gqa))*kv_head);
cb(k_cache_view, "k_cache_view", il);
struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_gqa,
@ -4011,8 +4011,8 @@ static struct ggml_tensor * llm_build_kqv(
struct ggml_tensor * k =
ggml_view_3d(ctx, kv.k_l[il],
n_embd_head, n_kv, n_head_kv,
ggml_type_sizef(kv.k_l[il]->type)*n_embd_gqa,
ggml_type_sizef(kv.k_l[il]->type)*n_embd_head,
ggml_row_size(kv.k_l[il]->type, n_embd_gqa),
ggml_row_size(kv.k_l[il]->type, n_embd_head),
0);
cb(k, "k", il);