ikawrakow/ik_llama.cpp
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Add optional MLA (#188)

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* Deepseek MLA Optimizations

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>

* Make MLA optional

* Remove some unnecessary copies in the MLA attention

* Deepseek MLA Optimizations V2 (#195)

* Avoid allocating MHA KV cache when MLA is turned on

* Added missing gguf-py file

* Added final optimizations

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>

* Make sure we do have wk_b and wv_b before enabling MLA

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

* Use type_k and type_v to set the types of the MLA caches

They were hard-coded at f16.
On my Ryzen-7950X with native bf16 support I get a fairly
significant PP performance boost with bf16 KV-cache:
PP-4096 = 320 t/s up from 292 t/s with fp16 KV-cache.

* Better gemm strategy when nth > nhead

It gives a ~10% PP performance boost for DeepSeek-Lite with 32 threads
(with or without MLA).
Before this commit, when nth > nhead heads were processed
sequentially with all nth threads participating in each
matrix multiplication. Now we ind the gcd of nhead and
nth and split threads into nth/gcd groups, each group
processing nhead/gcd heads.

---------

Co-authored-by: Saood Karim <saood05@gmail.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-02-09 19:48:44 +02:00
committed by GitHub
parent db7eabb111
commit 3e536b95b0
9 changed files with 380 additions and 75 deletions
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17
ggml/src/ggml.c
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@@ -14064,31 +14064,22 @@ static void ggml_compute_forward_mul_mat(
#endif
#if GGML_USE_IQK_MULMAT
if (dst->type == GGML_TYPE_F32 && (ne12*ne13)%nth == 0) {
if (dst->type == GGML_TYPE_F32) {
int gcd = simple_gcd(ne12*ne13, nth);
int counter = 0;
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
if (counter++ % nth == ith) {
if ((counter++ % gcd) == (ith%gcd)) {
if (!iqk_mul_mat(ne01, ne11, ne00,
src0->type, (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01, ///ggml_type_size(src0->type),
src1->type, (const char *)src1->data + i12*nb12 + i13*nb13, nb11, ///ggml_type_size(src1->type),
(float *)((char *)dst->data + i12*nb2 + i13*nb3), nb1/ggml_type_size(dst->type),
0, 1)) goto IQK_MulMat_Not_Available1;
ith/gcd, nth/gcd)) goto IQK_MulMat_Not_Available1;
}
}
}
return;
}
if (dst->type == GGML_TYPE_F32) {
for (int64_t i13 = 0; i13 < ne13; i13++)
for (int64_t i12 = 0; i12 < ne12; i12++)
if (!iqk_mul_mat(ne01, ne11, ne00,
src0->type, (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03, nb01, ///ggml_type_size(src0->type),
src1->type, (const char *)src1->data + i12*nb12 + i13*nb13, nb11, ///ggml_type_size(src1->type),
(float *)((char *)dst->data + i12*nb2 + i13*nb3), nb1/ggml_type_size(dst->type),
ith, nth)) goto IQK_MulMat_Not_Available1;
return;
}
IQK_MulMat_Not_Available1:;
#endif