mirrors/ollama-for-amd
1
0
Fork 0
mirror of https://github.com/likelovewant/ollama-for-amd.git synced 2025-12-22 06:43:57 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: llama.cpp bump (17f7f4) for SSM performance improvements (#13408)

Browse Source 
* feat: Bump llama.cpp to the latest master (17f7f4b)

This brings in significant improvements to prefill performance for all
models using the SSM_CONV and SSM_SCAN ops (granite4, jamba, falcon-h,
nemotron-h, Qwen3 Next) on Apple Metal.

See https://github.com/ggml-org/llama.cpp/pull/17876

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches 1-4

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Update patches 5-12

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches 13-18

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patch 20

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Update patches 21-31

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Sync vendored code

The two files I'm not sure about here are the swap from gemma3-iswa.cpp to
gemma3.cpp (I chose to include this because I think it's required), and the
inclusion of `ggml-zendnn.h` which I chose to omit.

Branch: LlamaCPPMetalSSMImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart
2025-12-10 13:59:27 -07:00
committed by GitHub
parent c34fc64688
commit b95693056c
115 changed files with 5176 additions and 2585 deletions
Download Patch File Download Diff File Expand all files Collapse all files

18
llama/llama.cpp/src/models/deepseek2.cpp vendored
View File

@@ -30,6 +30,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
// {n_embd, n_tokens}
inpL = build_inp_embd(model.tok_embd);
// (optional) temperature tuning - used by mistral-large
ggml_tensor * inp_attn_scale = nullptr;
if (hparams.f_attn_temp_scale != 0.0f) {
inp_attn_scale = build_inp_attn_scale();
}
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
@@ -128,6 +134,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
ggml_tensor * Vcur = kv_cmpr;
cb(Vcur, "Vcur", il);
if (inp_attn_scale) {
// apply llama 4 temperature scaling
Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
cb(Qcur, "Qcur_attn_temp_scaled", il);
}
// note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group)
cur = build_attn(inp_attn,
model.layers[il].wo, NULL,
@@ -160,6 +172,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
cb(Kcur, "Kcur", il);
if (inp_attn_scale) {
// apply llama 4 temperature scaling
Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
cb(Qcur, "Qcur_attn_temp_scaled", il);
}
// note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
cur = build_attn(inp_attn,
model.layers[il].wo, NULL,