Add optional MLA (#188)

* 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.

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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>

1 files changed, 7 insertions, 0 deletions

diff --git a/common/common.cpp b/common/common.cpp
index 95e91bc1..6219f0ce 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -813,6 +813,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.flash_attn = true;
         return true;
     }
+    if (arg == "-mla" || arg == "--mla-use") {
+        params.mla_attn = true;
+        return true;
+    }
     if (arg == "-co" || arg == "--color") {
         params.use_color = true;
         return true;
@@ -1452,6 +1456,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "*",           "       --keep N",               "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
     options.push_back({ "*",           "       --chunks N",             "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
     options.push_back({ "*",           "-fa,   --flash-attn",           "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
+    options.push_back({ "*",           "-mla,  --mla-use",              "enable MLA (default: %s)", params.mla_attn ? "enabled" : "disabled" });
     options.push_back({ "*",           "-p,    --prompt PROMPT",        "prompt to start generation with\n"
                                                                         "in conversation mode, this will be used as system prompt\n"
                                                                         "(default: '%s')", params.prompt.c_str() });
@@ -2283,6 +2288,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.cb_eval_user_data = params.cb_eval_user_data;
     cparams.offload_kqv       = !params.no_kv_offload;
     cparams.flash_attn        = params.flash_attn;
+    cparams.mla_attn          = params.mla_attn;
 
     cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
     cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
@@ -3280,6 +3286,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
     fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
     fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
+    fprintf(stream, "mla_attn: %s # default: false\n", params.mla_attn ? "true" : "false");
     fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
 
     const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());